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https://doi.org/10.1038/s41562-021-01173-x
A multi-country test of brief reappraisal
interventions on emotions during the COVID-19
pandemic
A full list of affiliations appears at the end of the paper.
The COVID-19 pandemic has increased negative emotions and decreased positive emotions globally. Left unchecked, these
emotional changes might have a wide array of adverse impacts. To reduce negative emotions and increase positive emotions,
we tested the effectiveness of reappraisal, an emotion-regulation strategy that modifies how one thinks about a situation.
Participants from 87 countries and regions (n = 21,644) were randomly assigned to one of two brief reappraisal interventions
(reconstrual or repurposing) or one of two control conditions (active or passive). Results revealed that both reappraisal
interventions (vesus both control conditions) consistently reduced negative emotions and increased positive emotions across
different measures. Reconstrual and repurposing interventions had similar effects. Importantly, planned exploratory analyses
indicated that reappraisal interventions did not reduce intentions to practice preventive health behaviours. The findings
demonstrate the viability of creating scalable, low-cost interventions for use around the world.
Protocol registration
The stage 1 protocol for this Registered Report was accepted in principle on 12 May 2020. The protocol, as accepted by the journal,
can be found at https://doi.org/10.6084/m9.figshare.c.4878591.v1
The COVID-19 pandemic is increasing negative emotions
and decreasing positive emotions around the globe1–10. Con-
currently, individuals are reporting that COVID-19 is having
a negative impact on their psychological functioning and mental
health4,11,12. For example, individuals report sleeping less, consum-
ing more alcohol or other drugs or substances, having trouble
concentrating because their mind is occupied by COVID-19, and
having more fights with their partner or loved ones, some escalating
to domestic violence1,9,13.
These disturbing trends are caused partly by heightened levels of
negative emotion and diminished levels of positive emotion, which
have been found to contribute to a number of negative psychological,
behavioural and health consequences. These include increased
risk of anxiety and depressive disorders as well as other forms of
psychopathology14; impaired social connections15; increased
substance use16–18; compromised immune system functioning19–21;
disturbed sleep22; increased maladaptive eating23,24; increased
aggressive behaviour25,26; impaired learning27; worse job perfor-
mance28,29; and impaired economic decision-making30,31.
As the COVID-19 pandemic unfolds around the world, we
believe it is crucial to mitigate expected adverse outcomes by reduc-
ing negative emotions and increasing positive emotions. Such a
change in emotions is central to increasing psychological resilience,
a multifaceted concept that involves adaptive emotional responses in
the face of adversity32–34. Reappraisal—an emotion regulation strat-
egy that involves changing how one thinks about a situation with
the goal of influencing one’s emotional response35—is a promising
candidate as an intervention to increase psychological resilience due
to its adaptability, simplicity and efficiency34,36–38. In contrast to less
effective emotion-regulation strategies such as suppression, reap-
praisal generally leads to more successful regulation (d = 0.45, 95%
confidence interval (CI) = [0.35, 0.56] in changing emotion experi-
ence in a meta-analysis39; see caveats about interpreting effect sizes
in past research in Methods, ‘Sampling plan’). In particular, over the
short term, reappraisal leads to decreased reports of negative emo-
tion and increased reports of positive emotion40–42, as well as cor-
responding changes both in peripheral physiological responses43–45
and central physiological responses46–53. Over the longer term, reap-
praisal is associated with stronger social connections54; higher aca-
demic achievement55,56; enhanced psychological well-being57; fewer
psychopathological symptoms58,59; better cardiovascular health60,61,
and greater resilience during the COVID-19 pandemic62.
Despite these shorter-term and longer-term benefits, most
people do not reappraise consistently63,64, which has motivated
efforts to teach people to use reappraisal (reviewed in refs. 65,66).
For example, in the context of anxiety, reappraisal training led to
reduced intrusive memories67 and increased emotion-regulation
self-efficacy68,69. Reappraisal training also led to long-lasting changes
in the neural representation of unpleasant events70.
Although demand characteristics are always a concern when
examining the effects of reappraisal (given that one is teaching
people to change their thinking in order to change how they’re
feeling, and then asking them how they feel)71, the wide array of
self-report and non-self-report outcomes39–53 that show reappraisal
effects across studies increases confidence that these effects are real.
It is also encouraging to note that reappraisal generally outperforms
other types of emotion regulation such as suppression, even though
demand characteristics appear comparable across regulation condi-
tions39. In addition, evidence indicates that reappraisal interventions
can influence emotional outcomes even in intensely challenging
contexts in which people are often unmotivated to regulate their
emotions72. For example, a brief reappraisal training conducted in
the context of the Israeli–Palestinian conflict and replicated in the
context of the Colombian conflict73, has been found to contribute
to reduced intergroup anger and increased support for conciliatory
political policies74.
As part of the attempt of the Psychological Science Accelerator
(PSA) to address pressing questions related to the psychological
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In testing these hypotheses, we planned to use orthogonal con-
trasts that make two primary comparisons, while keeping all other
comparisons exploratory (Table 1 provides further detail). The
first comparison contrasted both the reappraisal conditions com-
bined with both the active control condition and the passive control
condition combined for negative (hypothesis 1) and positive
(hypothesis 2) emotions. The second comparison contrasted the
reconstrual and repurposing interventions for negative (hypoth-
esis 3) and positive (hypothesis 4) emotions. One attractive feature
of comparisons between reappraisal conditions is that there is no
reason to assume that demand or expectancies would differ across
these reappraisal conditions.
One potential concern about the current design was that the
emotion-regulation interventions might reduce preventive health
behaviours (for example, maintaining social distance and washing
hands) that could potentially be motivated by negative emotions.
Some research on the connection between emotions and health
behaviour suggests that increased negative emotions such as fear
do not seem to be a strong motivator for changing one’s health
behaviour83. Furthermore, positive emotions augmented by the
reappraisal interventions may contribute to a greater tendency to
undertake health behaviours84,85. For example, positive emotions
can lead to higher medication adherence86. To ensure that our inter-
ventions would not adversely impact any relevant health behav-
iours, we took two steps. First, during the instructions, we clarified
that—in some cases—negative emotions such as fear and sadness
may be helpful, and that it is up to each person to determine when
an emotion is unhelpful or not and to downregulate only those
emotions that are unhelpful. Second, to assess whether our train-
ing would lead to reduced vigilance, we specifically measured and
examined intentions to follow stay-at-home orders and wash hands
in exploratory analyses.
In addition, we conducted other exploratory analyses. These
analyses included testing the impact of our reappraisal interven-
tions on negative and positive anticipated emotions and intentions
to enact potentially harmful versus beneficial behaviours associated
with these emotions (details described in Methods, ‘Measures’), and
assessed whether the effects of our reappraisal interventions, if any,
were moderated by motivation to use the given strategy71, belief in
the effectiveness of the given strategy87, or demographics (gender39,
socioeconomic status88,89 or country or region90 (hereafter coun-
try/region) (particularly in light of the differing levels of impact of
COVID-19 in any given country/region at any given point in time)).
Results
Final sample size and demographics. We collected 27,989
responses between May 2020 and October 2020. After implement-
ing preregistered exclusions (see detail in https://doi.org/10.6084/
m9.figshare.c.4878591.v1) and an additional exclusion of nine
duplicate IDs, our final sample included 21,644 participants from
87 countries/regions (63.41% female, 35.34% male, 0.45% other
genders, 0.56% preferred not to say and 0.24% missing responses
to the gender question; participants were aged 31.91 ± 14.52 yr
(mean ± s.d.); see Supplementary Table 1 for sample size per coun-
try/region and Supplementary Table 2 for sample size per month).
Of the 87 countries/regions represented, 37 had more than 200 par-
ticipants, surpassing our 95% power criterion based on simulations
in our power analysis (see detail in Methods, ‘Power analysis’).
We preregistered two exclusion criteria. First, as planned, we
excluded participants who answered both multiple choice manipu-
lation check questions incorrectly, and found that conditions had
similar proportions of such participants (0.55%), Holm’s adjusted
P values > 0.999. Second, as planned, we excluded participants
who completed fewer than 50% of the questions in the study, and
found that the passive control condition had fewer such parti-
cipants (16.17%) than the other three conditions (23.86% in the
impact of COVID-19, the current study aimed to use reappraisal
interventions to enhance psychological resilience in response to
the pandemic. To maximize the impact of these interventions, this
project had a global reach of large, diverse samples via the PSA’s net-
work75, and employed highly scalable methods that were translated
for use around the world. In order to make stronger and clearer
inferences, our design included two reappraisal interventions that
were compared with two control conditions, an active control and
a passive control.
For our reappraisal interventions, we examined two theoreti-
cally defined forms of reappraisal76—reconstrual and repurposing.
Reconstrual involves changing how a situation was construed or
mentally represented in a way that changes the emotional responses
related to the situation. Examples of reconstrual in response to
COVID-19 include: “Washing hands, avoiding touching my face,
keeping a safe distance…There are simple and effective things I can
do to protect myself and my loved ones from getting sick and to stop
the spread of the virus” and “I know from world history that keeping
calm and carrying on gets us through tough times”. Repurposing
involves focusing on a potentially positive outcome that could come
from the current situation in a way that changes the emotional
response to it. Examples of repurposing in response to COVID-19
include: “This situation is helping us realize the importance of
meaningful social connections, and helping us understand who
the most important people in our lives are” and “Medical systems
are now learning to deal with amazing challenges, which will make
them much more resilient in the future”. For our active control con-
dition, we asked participants to reflect on their thoughts and feel-
ings as they unfolded. Meta-analyses have revealed that reflecting
on one’s thoughts and feelings produces small but reliable salutary
effects (d = 0.07, 9% CI = [0.05, 0.17] in improving psychological
health, including emotional responses77,78). Examples of reflecting
in response to COVID-19 are: “I really wish we could find a vac-
cine soon” and “This situation is changing so fast, and I don’t know
how the future will develop”. By asking participants in this condi-
tion to actively use a strategy that is likely to have a positive effect,
we sought to match expectancy and demand across reappraisal and
active control conditions. For our passive control condition, we
asked participants to respond as they naturally do, which is a com-
monly used passive control condition in prior research on emotion
regulation (for a meta-analysis, see ref. 39).
In comparing conditions, we chose to distinguish between nega-
tive and positive emotional responses, as previous evidence suggests
that the two are clearly separable79,80. Specifically, we hypothesized
that our reappraisal interventions would lead to reduced negative
emotional responses (hypothesis 1) and increased positive emo-
tional responses (hypothesis 2) compared with both control condi-
tions combined. While both reconstrual and repurposing strategies
involve changing thinking, we hypothesized that the reconstrual
intervention would lead to greater decreases in negative emotional
responses than the repurposing intervention (hypothesis 3) and
that the repurposing intervention would lead to greater increases
in positive emotional responses than the reconstrual interven-
tion (hypothesis 4). We theorized that reconstruing one’s situation
should primarily decrease negative emotions, because it typically
focuses on ameliorating the problem at hand. The reconstrual inter-
vention is most similar to a previously studied subtype of reappraisal
called reappraising emotional stimulus, which has been investigated
mainly on negative emotions and has a d = 0.38 and 95% CI = [0.21,
0.55] for changing emotion experience39. Repurposing one’s situ-
ation, by contrast, should primarily increase positive emotions
because it usually calls to mind positive experiences. Repurposing is
similar to a few previously examined types of reappraisals, such as
benefit finding and positive reappraisal, both of which are primar-
ily associated with positive outcomes81,82 (Methods, ‘Sampling plan’
provides further detail).
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active control condition, 24.41% in the reconstrual condition and
23.90% in the repurposing condition), Holm’s adjusted P < 0.001.
One possible explanation for this difference is that the instructions
given to participants in the passive control condition were shorter
than those given in the other conditions, requiring less cognitive
effort to read and less time to complete the study. Applying both
exclusion criteria, the overall exclusion rate was significantly lower
in the passive control condition (16.71%) than in the other three
conditions (24.47% in the active control condition, 24.99% in the
reconstrual condition and 24.37% in the repurposing condition),
Holm’s adjusted P < 0.001. To rule out concerns related to differ-
ences in exclusion rates, we repeated all preregistered analyses on
the full sample. Reassuringly, all patterns, statistical significance and
conclusions remained unchanged when analyses were repeated on
the full sample (Supplementary Table 3).
Preregistered analyses. We included all 87 countries/regions
in all analyses regardless of their sample sizes, except for Fig. 1,
Supplementary Fig. 1 and Supplementary Fig. 2, where the 37 coun-
tries/regions with n ≥ 200 were analysed separately by country/
region. Effect sizes, frequentist statistics and Bayes factors for each
of our hypotheses are presented in Table 2. Raw means and standard
deviations for each relevant measure are provided in Table 3. Details
of analytical models are described in Methods.
Hypotheses regarding the shared effects of two brief reappraisal inter-
ventions. Consistent with the main hypotheses of the study, both
reappraisal interventions combined (versus both control conditions
combined) significantly decreased negative emotional responses
(hypothesis 1) and significantly increased positive emotional
responses (hypothesis 2) across all primary outcome measures
(emotions in response to the photos related to COVID-19 from
various news sources, state emotions after viewing all the photos and
emotions about the COVID-19 situation; Table 2, rows 2–7; details
of these measures are described in Methods). As shown in Fig. 1,
this finding was consistent across the 37 countries/regions in which
there were more than 200 participants (although all 87 countries/
regions were included in the analysis testing hypotheses regardless
of their sample size, only the 37 countries/regions with n ≥ 200 were
analysed separately by country/region for Fig. 1). For example, in
comparing participants’ immediate negative emotional responses
to the photos related to the COVID-19 situation, data from 33 out
of the 37 (89%) countries/regions showed significant effects of the
reappraisal interventions in the hypothesized direction. None of the
37 countries/regions’ data revealed a statistically significant result
in the opposite direction.
Hypotheses regarding the unique effects of the two reappraisal interven-
tions. Results revealed little to no support for our hypotheses regard-
ing the differences between reconstrual and repurposing, as neither
was reliably better than the other at reducing negative emotions or
increasing positive emotions across outcomes (Table 2, rows 8–13;
Supplementary Fig. 2). We hypothesized that reconstrual would
produce greater decreases in negative emotional responses than
repurposing (hypothesis 3), and data revealed supportive evidence
for only one outcome (negative emotions about the COVID-19
situation; Table 2, row 10) out of the three measures of negative
emotions. The other two negative emotion outcome measures did
not support that hypothesis. One outcome (negative emotions in
response to the photos; Table 2, row 8) revealed that repurposing
had significantly stronger effects in decreasing negative emotional
responses than reconstrual, whereas the Bayes factor indicated
inconclusive evidence. Another outcome (negative state emotions;
Table 2, row 9) revealed no significant difference between types of
reappraisal, and the Bayes factor indicated strong evidence in favour
of the null hypothesis.
We also hypothesized that repurposing would produce greater
increases in positive emotional responses than reconstrual (hypo-
thesis 4), and data revealed supportive evidence for only one out-
come (positive emotions in response to the photos; Table 2, row 11)
out of the three measures of positive emotions. The other two out-
come measures of positive emotions revealed no significant differ-
ences between the two reappraisal conditions. The Bayes factors
indicated strong evidence in favour of the null hypothesis for one
outcome (positive state emotions; Table 2, row 12) and inconclu-
sive evidence for another outcome (positive emotions about the
COVID-19 situation; Table 2, row 13). Overall, there were no
consistent differences across outcomes between reconstrual and
repurposing in reducing negative emotions or increasing positive
emotions in the current experimental context. We examined poten-
tial reasons for these findings in the exploratory analyses and in the
discussion section.
Exploratory analyses. To better understand the impact of the reap-
praisal interventions, we conducted four sets of exploratory analy-
ses. First, we examined pairwise comparisons between conditions
(each of the reappraisal conditions versus each of the control con-
ditions, and the active control condition versus the passive control
condition) for our primary outcomes (emotions in response to the
photos, state emotions after viewing all the photos and emotions
about the COVID-19 situation). Second, we assessed the effect of
reappraisal interventions on four exploratory outcomes (behav-
ioural intentions to practice preventive health behaviours, partici-
pants’ engagement with emotion regulation strategies, global change
in emotions, and anticipated emotions). Third, we assessed four
sets of potential moderators of reappraisal interventions’ effects
(motivation to use the given strategy71, belief in the effectiveness of the
given strategy87, demographics39,88–90 and lockdown status). Finally,
we contextualised reappraisal interventions’ effect sizes on negative
emotions by comparing them with effect sizes of lockdown status
and self-isolation due to symptoms. Details of analytical models
are reported in Supplementary Information (Supplementary Tables
4 and 5).
Pairwise comparisons of conditions on primary outcomes. In the
first set of exploratory analyses, we examined the extent to which
each of the reappraisal conditions differed from each of the control
conditions for our primary outcomes (emotions in response to the
photos, state emotions after viewing all the photos and emotions
about the COVID-19 situation). Pairwise comparisons for all pri-
mary outcomes produced results consistent with the pattern of evi-
dence for hypothesis 1 and hypothesis 2. Each of the repurposing
and reconstrual conditions (versus each of the control conditions)
significantly decreased negative emotional responses and signifi-
cantly increased positive emotional responses (Ps < 0.001; Table 3).
We also examined whether the active and passive control condi-
tions differed from each other at the level of pairwise comparisons.
Among the three primary outcome measures of negative emotional
responses, one was significantly higher in the active control con-
dition than in the passive control condition (negative emotions
Table 1 | Contrast structure of testing hypotheses 1–4 (with unit
weighting)
Active
control Passive
control Reconstrual Repurposing
Contrast 1
(hypotheses 1–2) 0.5 0.5 −0.5 −0.5
Contrast 2
(hypotheses 3–4) 0 0 0.5 −0.5
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Emotions in response to
the photos related to COVID-19
"How negative (positive) did
the photo make you feel?"
State emotions after viewing
all the photos related to COVID-19
"How are you feeling right now?"
Emotions about the COVID-19 situation
"Overall, how negative/hopeless
(positive/hopeful) are you feeling
about the COVID-19 situation right now?"
−1.0 −0.5 0 0.5 1.0 −1.0 −0.5 0 0.5 1.0 −1.0 −0.5 0 0.5 1.0
Egypt
Romania
Russia
Pakistan
China
Poland
Japan
Sweden
Switzerland
South Africa
Croatia
Belgium
Slovenia
Netherlands
South Korea
Mexico
Armenia
Portugal
Canada
Costa Rica
Turkey
UK
Australia
Nigeria
Norway
Kenya
France
Philippines
USA
Chile
Czechia
Austria
Italy
Slovakia
Hungary
Germany
Brazil
Mean effect
Both reappraisal interventions combined (versus both control conditions combined)
rating differences on 5-point scales with 95% confidence interval
Valence Negative Positive Sample size 200 800 2,400
Fig. 1 | Effect sizes of both reappraisal interventions combined (versus both control conditions combined) on primary outcomes by country/region.
In almost all of the 37 countries/regions in which there were more than 200 participants, both reappraisal interventions combined (versus both control
conditions combined) decreased negative emotional responses and increased positive emotional responses for primary outcome measures (emotions in
response to the photos, state emotions after viewing all the photos, and emotions about the COVID-19 situation). Effect sizes are raw mean differences
on five-point scales without adjusting for covariates. Confidence intervals are based on the t distribution. Countries/regions are ordered by decreasing
effect sizes of negative emotions in response to the photos, and larger dots reflect larger samples (Supplementary Fig. 1 presents the countries/regions in
alphabetical order.).
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Table 2 | Effect sizes, frequentist statistics and Bayes factors for each preregistered hypothesis
Row number Hypothesis B (s.e.m.) Standard deviation of
B by country/region
t statistic (d.f.) Holm’s adjusted
P value
Cohen’s d [95% CI] log10(BF) [under
robustness check]
Verbal interpretation132 of log10(BF)
2 Reappraisal interventions (versus control)
would reduce negative emotions in response
to the photos (hypothesis 1a).
0.513 (0.021) 0.129 23.973 (52.36) <0.001 0.392 [0.360, 0.425] 29.41 [29.47] log(BF)>2 represents “extreme
evidence in favour of HA”;
2>log(BF)>1.5 represents “very strong
evidence in favour of HA”;
1.5>log(BF)>1 represents “strong
evidence in favour of HA”;
1>log(BF)>0.5 represents “moderate
evidence in favour of HA”;
0.5>log(BF)>−0.5 represents
“inconclusive evidence”;
−0.5>log(BF)>−1 represents
“moderate evidence in favour of H0”;
−1>log(BF)>−1.5 represents “strong
evidence in favour of H0”;
−1.5>log(BF)>−2 represents “very
strong evidence in favour of H0”;
−2>log(BF) represents “extreme
evidence in favour of H0”.
3 Reappraisal interventions (versus control)
would reduce negative state emotions
(hypothesis 1b).
0.185 (0.013) 0.064 14.401 (36.39) <0.001 0.313 [0.270, 0.357] 15.61 [15.15]
4 Reappraisal interventions (versus control)
would reduce negative emotions about the
COVID-19 situation (hypothesis 1c).
0.241 (0.019) 0.082 12.570 (30.67) <0.001 0.239 [0.201, 0.277] 13.26 [12.92]
5 Reappraisal interventions (versus control)
would increase positive emotions in response
to the photos (hypothesis 2a).
0.711 (0.025) 0.166 28.301 (59.18) <0.001 0.590 [0.549, 0.631] 34.65 [34.80]
6 Reappraisal interventions (versus control)
would increase positive state emotions
(hypothesis 2b).
0.178 (0.012) 0.064 14.263 (42.69) <0.001 0.326 [0.281, 0.372] 15.90 [15.42]
7 Reappraisal interventions (versus control)
would increase positive emotions about the
COVID-19 situation (hypothesis 2c).
0.263 (0.018) 0.070 14.809 (31.21) <0.001 0.266 [0.230, 0.301] 15.48 [15.23]
8 Reconstrual would lead to greater decreases
in negative emotional responses in response
to the photos than repurposing (hypothesis
3a).
−0.056 (0.023) 0.107 −2.438 (33.48) 0.041 −0.043 [−0.078, −0.008] 0.25 [−0.47]
9 Reconstrual would lead to greater decreases
in negative state emotions than repurposing
(hypothesis 3b).
−0.005 (0.016) 0.069 −0.321 (29.67) 0.751 −0.008 [−0.063, 0.046] −1.09 [−1.87]
10 Reconstrual would lead to greater decreases
in negative emotions about the COVID-19
situation than repurposing (hypothesis 3c)
0.068 (0.022) 0.045 3.139 (30.61) 0.011 0.067 [0.024, 0.112] 1.02 [0.32]
11 Repurposing would lead to greater increases
in positive emotions in response to the photos
than reconstrual (hypothesis 4a).
0.137 (0.022) 0.113 6.176 (46.79) <0.001 0.114 [0.077, 0.151] 5.37 [4.84]
12 Repurposing would lead to greater increases
in positive state emotions than reconstrual
(hypothesis 4b).
−0.006 (0.011) Random slopes by
country/region were not
included for the model to
converge
−0.526 (20,340) 0.599 −0.011 [−0.049, 0.02 8] −1.39 [−2.00]
13 Repurposing would lead to greater increases
in positive emotions about the COVID-19
situation than reconstrual (hypothesis 4c).
−0.047 (0.026) 0.109 −1.781 (37.46) 0.166 −0.047 [−0.100, 0.005] −0.41 [−0.93]
All 87 countries/regions were included in the preregistered analyses regardless of their sample sizes. The signs of B, t-statistic and Cohen’s d are adjusted such that positive (negative) values indicate being consistent (inconsistent) with the direction
specified in a hypothesis. For hypotheses 1–2, B reflects the difference on the original five-point scales between the average of the means of the two control conditions and the average of the means of the two reappraisal intervention conditions. For
hypotheses 3–4, B reflects the difference on the original five-point scales between the mean of the reconstrual condition and the mean of the repurposing condition. Degrees of freedom (d.f.) vary due to random slopes133. Cohen’s d is calculated as the
raw mean difference divided by the square root of the pooled variance of all the random components. HA, alternative hypothesis; H0, null hypothesis.
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in response to the photos: B = 0.091 ± 0.015, t(20,740) = 6.192,
P < 0.001, d = 0.070, 95% CI = [0.048, 0.093]), while the other
two showed no significant differences (negative state emo-
tions: B = 0.022 ± 0.011, t(20,400) = 1.933, P = 0.053, d = 0.037,
95% CI = [−0.001, 0.075]; negative emotions about the COVID-19
situation: B = 0.005 ± 0.022, t(26.01) = 0.221, P = 0.827, d = 0.005,
95% CI = [−0.040, 0.047]). Among the three primary outcome mea-
sures of positive emotional responses, two were significantly higher
in the active control condition than in the passive control condi-
tion (positive emotions in response to the photos: B = 0.039 ± 0.013,
t(20,740) = 2.918, P = 0.004, d = 0.033, 95% CI = [0.011, 0.054];
positive emotions about the COVID-19 situation: B = 0.053 ± 0.019,
t(233.7) = 2.805, P = 0.005, d = 0.053, 95% CI = [0.015, 0.091]),
while one showed no significant differences (positive state emo-
tions: B = 0.009 ± 0.010, t(20,350) = 0.858, P = 0.391, d = 0.017,
95% CI = [−0.021, 0.054]). Thus, effects produced by the active
control condition versus the passive control condition differed
infrequently. When they did differ, differences were small in mag-
nitude, inconsistent in direction, and slightly smaller in effect
size than was suggested by previous meta-analyses77 (d = 0.07,
95% CI = [0.05, 0.17]).
Effects of reappraisal interventions on four exploratory outcomes.
Details of exploratory outcomes can be found in Methods and Fig. 2.
Descriptive statistics and pairwise comparisons for exploratory
outcomes can be found in Table 3. Here we focus on the contrast
between the two reappraisal interventions combined and the two
control conditions combined.
Behavioural intentions to practice preventive health behaviours.
To address the concern that reappraisal interventions might reduce
preventive health behaviours (by reducing negative emotions such
as fear), we asked about participants’ behavioural intentions to
follow stay-at-home orders stringently and to wash their hands
regularly for at least 20 s the following week. We found that reap-
praisal interventions (versus both control conditions combined)
did not significantly change intentions to follow stay-at-home
orders (B = 0.009 ± 0.024, t(15.04) = 0.38, P = 0.709, d = 0.005,
95% CI = [−0.023, 0.032]) or to wash hands (B = 0.034 ± 0.020,
t(20,740) = 1.69, P = 0.091, d = 0.022, 95% CI = [−0.004, 0.048]).
Pairwise comparisons revealed that the only significant difference
was that participants in the reconstrual condition reported higher
intentions to wash their hands than those in the passive control
condition (B = 0.077 ± 0.028, t(20,740) = 2.714, Holm’s adjusted
P = 0.040, d = 0.051, 95% CI = [0.014, 0.087]). These results thus
provide preliminary evidence that reappraisal interventions did
not significantly reduce intentions to practice preventive health
behaviours.
Participants’ engagement with emotion-regulation strategies. To bet-
ter understand participants’ engagement with emotion-regulation
strategies when viewing the photos related to COVID-19, we exam-
ined participants’ self-reported frequency of using different strate-
gies when viewing the photos, motivation to use their given strategy,
and belief in the effectiveness of their given strategy.
Providing confidence in the effectiveness of the manipulation,
we found that participants in each of the four conditions reported
Table 3 | Raw mean and s.d. values for outcomes
Outcome Reappraisal interventions Control conditions
Reconstrual
(n=5,078) Repurposing
(n=5,421) Active control
(n=5,349) Passive control
(n=5,796)
Primary outcomes
Negative emotions in response to the photos 2.77a (0.80) 2.71b (0.77) 3.29c (0.83) 3.19d (0.84)
Positive emotions in response to the photos 2.47a (0.81) 2.62b (0.79) 1.86c (0.72) 1.84d (0.73)
Negative state emotions 2.32a (0.90) 2.31a (0.90) 2.52b (0.95) 2.48b (0.95)
Positive state emotions 3.17a (0.88) 3.18a (0.87) 2.99b (0.88) 2.98b (0.90)
Negative emotions about the COVID-19 situation 2.71a (1.08) 2.77b (1.07) 2.99c (1.10) 2.97c (1.10)
Positive emotions about the COVID-19 situation 2.91a (1.05) 2.88a (1.04) 2.65b (1.06) 2.59c (1.06)
Exploratory outcomes
Intention to follow stay-at-home orders stringently 5.42a (1.79) 5.44a (1.77) 5.41a (1.80) 5.45a (1.77)
Intention to wash hands regularly for at least 20s 5.82a (1.53) 5.82ab (1.50) 5.82ab (1.51) 5.76b (1.56)
Frequency of natural response 3.49a (1.35) 3.53b (1.35) 4.00c (1.17) 4.56d (0.79)
Frequency of using reflecting 3.92a (1.11) 3.90a (1.14) 4.25b (0.97) 3.91a (1.20)
Frequency of using reconstrual 3.80a (1.09) 3.73b (1.14) 3.06c (1.27) 2.75d (1.34)
Frequency of using repurposing 3.89a (1.13) 4.15b (1.01) 3.21c (1.31) 3.12d (1.34)
Motivation to use the given strategy 6.14a (1.12) 6.17a (1.12) 6.26b (1.04) 6.43c (1.00)
Belief in the effectiveness of the given strategy 5.00a (1.68) 5.03a (1.69) 4.80b (1.76) 4.44c (1.90)
Global change in negative feelings 2.82a (0.94) 2.75b (0.93) 3.19c (0.92) 3.17c (0.88)
Global change in positive feelings 3.28a (0.91) 3.33a (0.91) 2.92b (0.92) 2.92b (0.89)
Anticipated negative emotions 2.31a (0.90) 2.30a (0.89) 2.45b (0.92) 2.44b (0.94)
Anticipated positive emotions 3.26a (0.88) 3.26a (0.87) 3.13b (0.86) 3.11b (0.89)
Values are displayed as raw mean (s.d.). Sample sizes (n) presented in the second row reflect the numbers of participants after preregistered exclusion. Sample sizes vary by outcome because we dropped
incomplete cases on an analysis-by-analysis basis. All primary outcomes were assessed on five-point scales. The following four exploratory outcomes were assessed on seven-point scales: intention to
follow stay-at-home orders stringently, intention to wash hands regularly for at least 20s, motivation to use the given strategy, and belief in the effectiveness of the given strategy. The remaining exploratory
outcomes were assessed on five-point scales. Within each row, means that do not share a superscript differ at P<0.05; two-tailed, Holm’s method for adjustment. For instance, means both marked with a
do not differ significantly, but means marked with a and b differ significantly from each other.
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using the strategy instructed in their condition more frequently
than using the other strategies (see Table 3). It is noteworthy that
participants in the two reappraisal conditions reported using both
reconstrual and repurposing more frequently than those in either
control conditions rather than primarily using only the form of
reappraisal instructed in their condition. This finding may help
explain the lack of differences between the two reappraisal condi-
tions on our primary outcomes.
Next, we examined participants’ motivation to follow the given
instructions, as well as participants’ belief that the given strategy
could influence their emotions. We found that participants in the
two reappraisal interventions (versus both control conditions com-
bined) reported being significantly less motivated to follow their
given instructions while viewing the photos (B = −0.192 ± 0.016,
t(20.87) = −11.62, P < 0.001, d = −0.183, 95% CI = [−0.215,
−0.152]), but reported significantly greater belief in the effective-
ness of their given strategy (B = 0.420 ± 0.053, t(52.05) = 7.97,
P < 0.001, d = 0.233, 95% CI = [0.175, 0.290]). Thus, the reappraisal
conditions were effective in changing emotions despite the fact that
participants in reappraisal conditions reported being less motivated
to follow the instructions than participants in the control conditions.
Global change of emotions. At the end of the study, we asked
participants how they felt compared with at the beginning of the
study. We found that reappraisal interventions (versus both control
conditions combined) significantly reduced global negative
feelings (B = −0.397 ± 0.026, t(45.29) = 15.30, P < 0.001, d = −0.432,
95% CI = [−0.489, −0.377]) and significantly increased global
positive feelings (B = 0.378 ± 0.023, t(45.49) = 16.75, P < 0.001,
d = 0.423, 95% CI = [0.373, 0.473]). These findings suggest that the
effects are not specific to items in the immediate proximity of the
manipulations.
Anticipated emotions. To gain insight into the potential longer-term
effects of reappraisal interventions, we asked participants how
they anticipated they would feel the following week. We found
that reappraisal interventions (versus both control conditions
combined) significantly reduced negative anticipated emotions
(B = −0.125 ± 0.012, t(41.99) = −10.27, P < 0.001, d = −0.205,
95% CI = [−0.245, −0.166]) and significantly increased positive
anticipated emotions (B = 0.125 ± 0.008, t(13.07) = 15.58, P < 0.001,
d = 0.227, 95% CI = [0.197, 0.256]). These findings suggest that par-
ticipants anticipated that reappraisal strategies would be useful in
improving their emotional well-being in the future.
Exploratory moderators of intervention effects. Prior research sug-
gests that emotion-regulation interventions lead to better results
when the participants are: motivated to regulate their emotions71,
led to believe in the effectiveness of regulation87, female (versus
male)39, from lower (versus higher) socioeconomic status88,89, and
from Western (versus Eastern) cultures90. We examined these as
well as lockdown status (as a proxy for differing levels of impact
of COVID-19) as potential moderators on our primary outcomes
(emotions in response to the photos, state emotions after viewing
all the photos, and emotions about the COVID-19 situation).
Controlling for baseline emotions, results of multilevel models
revealed that two of the variables moderated intervention effects
across all six primary outcomes. Specifically, the higher the scores
on motivation to use the given strategy and on belief in the effec-
tiveness of the given strategy were, the more effective the interven-
tions were (Supplementary Figs. 3 and 4 and Supplementary Tables
6 and 7). Two variables (gender and employment status) moderated
intervention effects on four of the six primary outcomes: Females
(versus males) and individuals who had no employment and no
income (versus those who had employment and income or versus
those with no employment but with income) showed stronger
effects of the intervention (Supplementary Tables 9 and 10). One
variable moderated intervention effects on two of the six outcomes:
the higher a country/region scored on Hofstede’s91 index of indi-
vidualism, the more effective the intervention was in increasing
positive emotions in response to the photos and increasing posi-
tive emotions about the COVID-19 situation among participants
from that country/region (Supplementary Table 8). Subjective
socioeconomic status, education level, and lockdown status sig-
nificantly moderated no more than one of the six outcomes, which
would be unlikely to hold after correction for multiple comparisons
(Supplementary Tables 11–13). Full, detailed results are reported in
the Supplementary Information.
Contextualising reappraisal interventions’ effect sizes. To facilitate
interpretation of reappraisal effect sizes, it is helpful to compare
them to effect sizes of other factors that may have also contributed
to differences in participants’ emotions. One such candidate for
comparison is differences in emotional experience as a function of
lockdown status and of self-isolation due to symptoms. Assuming
that lockdown or self-isolation due to symptoms impacted partici-
pants’ emotions, emotional changes caused by these factors could be
compared to the ones caused by our interventions in order to get a
sense of the impact of our intervention.
With negative state emotions as the outcome variable, we
examined lockdown status and self-isolation due to symptoms,
respectively, as a fixed variable in two separate multilevel models
with random by-country/region slopes and random by-country/
region intercepts to estimate the pure effect size of each variable
(as lockdown status and self-isolation due to symptoms were
correlated, entering both variables simultaneously in the same
model may generate biased estimates). We found that participants
whose areas were in full lockdown reported more negative state
emotions than participants whose areas were not in lockdown
(B = 0.154 ± 0.040, t(37.56) = 3.812, P < 0.001, d = 0.159, 95% CI =
[0.075, 0.243]), and participants whose areas were in partial lock-
down reported more negative state emotions than participants
Pre-measure
baseline emotions
Randomization to
condition
Practice trials × 2
1. View a photo (≥ 10 s)
2. Rate a photo
3. Write the strategy used
4. Read an example how
the strategy might be
used
Experimental trials × 10
1. View a photo (≥ 10 s)
2. Rate a photo
Repurposing
Active control
Passive control
Post-measures
1. State emotions
2. Emotions about the
COVID-19 situation
3. Anticipated emotions
4. Behavioural intentions
5. Motivation/beliefs
6. Manipulation check
Reconstrual
Fig. 2 | Overview of the experiment. Participants in the passive control
condition did not have the fourth step in the practice trials.
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whose areas were not in lockdown (B = 0.094 ± 0.027, t(27.25) =
3.531, P = 0.001, d = 0.097, 95% CI = [0.041, 0.155]). We also
found that participants who were self-isolating due to flu-like or
cold-like symptoms reported more negative state emotions than
participants who were not self-isolating due to flu-like or cold-like
symptoms (B = 0.175 ± 0.044, t(25.83) = 3.981, P < 0.001, d = 0.183,
95% CI = [0.092, 0.283]). As shown in Table 2 for hypothesis 1b,
participants who were in the two reappraisal conditions reported
less negative state emotions than participants who were in the two
control conditions (B = 0.185 ± 0.013, t(36.39) = 14.401, P < 0.001,
d = 0.313, 95% CI = [0.270, 357]). In addition, the amount of vari-
ance explained by fixed effects in a model with only lockdown status
as a fixed variable is marginal92 R2 = 0.003. The amount of variance
explained by fixed effects in a model with only self-isolation due to
symptoms as a fixed variable is marginal92 R2 = 0.001. The amount
of variance explained by fixed effects in a model with only the con-
trast between the two reappraisal conditions and the two control
conditions as the fixed variable is marginal92 R2 = 0.013. Across
different measures of effect size, it is notable that the effects of
reappraisal interventions on state negative emotions were of simi-
lar or even larger magnitude than the effects of lockdown status
or self-isolation due to symptoms. This comparison suggests that
reappraisal interventions could help to alleviate the emotional toll
caused by lockdown and self-isolation. Thus, we believe that the
effects of reappraisal interventions are not only statistically signifi-
cant but also practically meaningful.
Discussion
The current study had two main goals. The first was to examine
the shared effects of two brief reappraisal interventions (versus both
passive and active control conditions) on negative and positive emo-
tions in response to the COVID-19 pandemic, and to determine
whether these effects were similar or different across countries/
regions and COVID-19 situations. The second goal was to examine
the potentially unique effects of the two reappraisal interventions—
reconstrual and repurposing—on negative and positive emotions.
Regarding the first goal, we predicted and found that both reap-
praisal interventions (versus both control conditions combined)
consistently decreased negative emotional responses (hypothesis 1)
and consistently increased positive emotional responses (hypoth-
esis 2) across all primary outcome measures: immediate emotions
in response to each photo about the COVID-19 situation, state
emotions after viewing all the photos related to the COVID-19
situation and overall emotions about the COVID-19 situation.
Exploratory analyses suggested that both reappraisal interventions
also improved participants’ reported emotions compared with at the
beginning of the study and the emotions they anticipated feeling in
the future.
Further exploratory analyses suggested that despite substantial
local variations in how severe the pandemic was at the time data
were collected and cultural differences in how people understand
and respond to emotions90,93, the intervention effects appeared in
almost all of the countries/regions we studied. For example, in com-
paring participants’ immediate negative emotional responses to the
photos related to the COVID-19 situation, 33 out of the 37 (89%)
countries/regions with high statistical power (over 200 participants)
showed statistically significant effects of reappraisal interventions.
Although reappraisal interventions tended to have larger effects
among females (versus males), and among unemployed individuals
without income, the effects were largely unqualified by education
level, subjective socioeconomic status, and whether a participant’s
country/region was under lockdown.
Regarding the second goal, we predicted that reconstrual would
be more effective at reducing negative emotions than repurposing
(hypothesis 3), but repurposing would be more effective at increas-
ing positive emotions than reconstrual (hypothesis 4). We found little
to no support for these hypotheses, as neither was reliably better
than the other at reducing negative emotions or increasing positive
emotions across outcomes. The finding that the two forms of reap-
praisal were similarly effective at regulating emotions in the context
of COVID-19 is consistent with the idea that the pandemic offers a
wide array of affordances both for construing emotional situations
in different ways, thus enabling reconstrual, and for evaluating these
situations in light of different goals, thus enabling repurposing76.
This implies that it may be beneficial to combine both strategies, a
hypothesis that future studies can be designed to test. It also remains
to be investigated whether reconstrual and repurposing offer simi-
larly comparable benefits in other contexts.
The comparable effectiveness of reconstrual and repurposing in
this context raises interesting questions about these two forms of
reappraisal. We found that even though participants learned only
one form of reappraisal, they reported using both strategies more
often than in either control condition. This overlap might have
stemmed from insufficient differentiation between the reappraisal
instructions used in this study. It may also mean that the distinction
between repurposing and reconstrual, although useful theoretically,
is not readily accessible to lay people. Alternatively, this overlap
may have stemmed from reconstrual and repurposing being mutu-
ally associated to a degree that being instructed to use one strategy
primes the other strategy. Future research is needed to more directly
investigate these possibilities.
After assessing results related to the primary goals, an important
question was whether reducing negative emotions and increasing
positive emotions in response to the pandemic might inadvertently
come at the cost of decreasing intentions to engage in preventive
health behaviour (reviewed in ref. 94). Reassuringly, the reappraisal
interventions improved emotions without significantly reducing
intentions to practice preventive health behaviours. This is consis-
tent with recent findings that there are many paths to motivate pre-
ventive health behaviours during the COVID-19 pandemic without
inducing negative emotions95–98.
Our results highlight the benefits of applying reappraisal inter-
ventions at scale to increase psychological resilience and to miti-
gate the adverse impacts of the COVID-19 pandemic—benefits
that could potentially be applied in other contexts that elicit nega-
tive emotions. Importantly, the effects of the intervention were not
meagre: the extent to which emotions were changed by our reap-
praisal interventions was comparable in magnitude to the extent to
which emotions differed between people who faced extreme hard-
ships (lockdowns or symptom-induced isolations) and people who
experienced neither of these hardships. Thus, contextualising the
effect sizes of reappraisal interventions in this manner suggests that
the interventions are practically meaningful. This practical meaning
matters in light of findings that people on average do not appear to
fully recover their emotional well-being even after six months into
the COVID-19 pandemic99, that stress and depression can impair
vaccine efficacy100, and that negative emotions predispose morbidity
and mortality via increases in substance use and other risky behav-
iours101. Essential workers, nurses and doctors, students, patients
and many other populations whose work and life are highly affected
by the pandemic could potentially benefit from reappraisal inter-
ventions, although more research is needed to establish the effec-
tiveness of reappraisal for groups facing distinct challenges. Because
these interventions are inexpensive, brief and scalable, they could
be implemented through a variety of media and communication
mechanisms, such as advertising campaigns102, speeches, courses,
apps and mobile games103.
Our results also have important implications for the science of
emotion (reviewed in ref. 104) and for emotion regulation (reviewed
in refs. 35,39) in particular. Despite the fact that reappraisal is one of
the most researched topics in psychology35, this study is the largest
cross-cultural investigation of reappraisal that has been conducted
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to date, drawing diverse samples from well beyond the WEIRD
(western, educated, industrialized, rich and democratic) societies105
that have been heavily represented to date in social science. Thus,
the findings reveal the generalizability of reappraisal effects across
many countries/regions even in the context of substantial, pro-
tracted stressors. The present study also extends understanding
of how contextual moderators influence reappraisal processes (for
example, individualism, lockdown status and demographics) while
deepening understanding of distinct forms of reappraisal (that is,
comparing them in relation to multiple outcomes). Finally, our
study provides a rich dataset for examining many other questions
related to emotions, emotion regulation and cultural differences.
We look forward to seeing what other insights can be generated
from this dataset.
Despite the encouraging findings, several limitations should
be noted. One limitation is the use of convenience sampling and
a limited set of photos. Our sample was not nationally representa-
tive within each country/region, and it appeared to over-represent
females, younger people and people with internet access. The photos
used in the study, although carefully chosen, might not represent
local situations for different groups of participants. Future research
is needed to assess generalizability using nationally representa-
tive samples and more personally emotionally evocative stimuli. A
second limitation is that we cannot fully rule out the influence of
demand characteristics and expectancies. Although we attempted
to match demand characteristics and expectancies in the reappraisal
conditions using our active control condition, we did not quantify
the extent to which they were comparable, and we measured per-
ceived strategy effectiveness after participants had used the strate-
gies, which is different from expectancies formed upon reading the
instructions but before using the strategies. Future research should
assess the influence of demand characteristics and expectancies.
A third limitation relates to the fact that the current study exam-
ined only the immediate and proximal effects of the interventions.
Future research employing longitudinal designs is needed to exam-
ine whether the effects persist over time and at what intervals indi-
viduals might optimally engage in reappraisal. A fourth limitation
is that the current study examined only a limited number of out-
comes via self-report measures. More comprehensive evaluations,
including assessments of actual behaviours (rather than intentions)
and health outcomes, are necessary to determine whether there are
any additional benefits or unintended consequences of the inter-
ventions. Finally, before implementing reappraisal interventions
for practice, more research is needed to better evaluate the inter-
vention (for example, via formal cost-benefit analysis and/or using
the ‘reach, efficacy, adoption, implementation and maintenance’
framework106,107).
In conclusion, our findings demonstrated that two brief reap-
praisal interventions had robust and generalizable effects in reduc-
ing negative emotions and increasing positive emotions during the
COVID-19 pandemic across countries/regions, without reduc-
ing intentions to practice preventive health behaviours. We hope
this study will inform efforts to create scalable interventions for
use around the world to build resilience during the pandemic and
beyond.
Methods
Ethics information and participants. is study is one of three studies in the PSA
COVID-19 Rapid Project. e other two studies investigated the eects of loss and
gain message framing and self-determination theory-guided message framing,
respec tively. e other two studies are reported elsewhere. e study was conducted
online, and participants clicked a single data collection link that led to either the
current study or the other two studies in the COVID-19 Rapid Project. A compre-
hensive summary of the PSA COVID-19 Rapid Project—including descriptions of
the study selection procedure, the other selected studies, the internal peer review
process, and implementation plans—can be found at https://psyarxiv.com/x976j/.
Participants were recruited by the PSA network. The PSA recruited 186
member laboratories from 55 countries/regions speaking 42 languages. Of the
27,989 participants recruited to complete the current study (not counting
participants for the other two studies in the PSA COVID-19 Rapid Project), 4,050
were recruited through semi-representative panelling (on the basis of sex, age
and sometimes ethnicity) from the following countries/regions: Egypt, Kenya,
Nigeria, South Africa, Mexico, United States, Austria, Romania, Russia, Sweden,
Switzerland, United Kingdom, China, Japan and South Korea (270 participants per
country/region). The remaining participants were recruited through the research
groups by convenience sampling. Each research group obtained approval from
their local Ethics Committee or IRB to conduct the study, explicitly indicated that
their institution did not require approval for the researchers to conduct this type
of task, or explicitly indicated that the current study was covered by a pre-existing
approval. Although the specifics of the consent procedure differed across research
groups, all participants provided informed consent. The style and the amount of
compensation varied with local conventions (a common practice in PSA). More
information regarding participant compensation and sample size can be found at
https://psyarxiv.com/x976j/.
Procedure. An overview of the experiment is depicted in Fig. 2.
Pre-measure. Before reading the instructions, participants reported emotions they
felt in the moment (details for all study measures are described in the next section).
These ratings constituted a baseline emotional measure.
Randomization to condition. Following the pre-measure, participants were randomly
assigned to one of four between-subjects experimental conditions: two reappraisal
intervention conditions (reconstrual and repurposing), one active control condition
and one passive control condition. Because the study was conducted online, data
collection was performed blind to the conditions of the participants. The content of
the instructions in each condition differed, but the lengths were matched except for
the passive control condition, which had a shorter set of instructions.
Participants in the two reappraisal intervention conditions (reconstrual and
repurposing) and the active control condition received the following instructions:
“In this study, we will show you photographs related to COVID-19 from various
news sources. Our goal is to better understand how people respond to such photos,
which may include feelings of fear, anger, and sadness. Sometimes emotions like
these are helpful. At other times, however, these emotions can be unhelpful to us.
Researchers have found that when people think their emotions are unhelpful, they
can take steps to influence their emotions.”
In the reconstrual condition, participants were told that (emphasis in original)
“One strategy that some people find helpful for influencing their emotions is
rethinking. This strategy involves changing one’s thinking in order to change one’s
emotions. This strategy is based on the insight that different ways of interpreting
or thinking about any situation can lead to different emotions. This means that
finding new ways of thinking about a situation can change how you feel about the
situation. For example, consider someone who stays at home under lockdown due
to COVID-19 and is feeling anxious, sad, or angry. In this case, rethinking might
involve realizing that the situation is only temporary because dedicated people
across the world are working hard to find a vaccine.” Participants were then given
four examples of how rethinking might be employed for the COVID-19 situation
(Example 1: “I know from world history that keeping calm and carrying on gets us
through tough times.”; Example 2: “Scientists across the world are working hard to
find treatment and vaccines. Throughout history, humans have been resourceful
in finding solutions to new challenges.”; Example 3: “Washing hands, avoiding
touching my face, keeping a safe distance…There are simple and effective things I
can do to protect myself and my loved ones from getting sick and to stop the spread
of the virus.”; Example 4: “In the past, people have overcome many challenges that
seemed overwhelming at the time, and we will overcome
COVID-19 related challenges too.”).
In the repurposing condition, participants were told that (emphasis in original)
“One strategy that some people find helpful for influencing their emotions is
refocusing. This strategy involves changing one’s thinking in order to change one’s
emotions. This strategy is based on the insight that finding something good in even
the most challenging situations can lead to different emotional responses. This
means that refocusing on whatever good aspects may be found in a situation can
change how you feel about the situation. For example, consider someone who stays
at home under lockdown due to COVID-19 and is feeling anxious, sad, or angry.
In this case, refocusing might involve realizing that staying at home gives them time
to do things that they may not have been able to do before, like reading, painting,
and spending time with family.” Participants were then given four examples of
how refocusing might be employed for the COVID-19 situation (Example 1: “This
situation is helping us realize the importance of meaningful social connections,
and helping us understand who the most important people in our lives are.”;
Example 2: “Medical systems are now learning to deal with amazing challenges,
which will make them much more resilient in the future.”; Example 3: “Even
though we are physically apart, we are finding creative ways to stay connected and
our hearts are more connected than ever.”; Example 4: “I have been inspired by the
way that frontline health care workers have responded with resilience, generosity,
determination, and deep commitment.”).
In the active control condition, participants were asked to reflect on their
emotions as they unfold. This condition is inspired by the literature on expressive
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writing and experimental disclosure, which shows that asking people to reflect
about their very deepest thoughts and feelings can improve psychological
health77,78. By having an active control condition, which was likely to lead to some
benefit to participants, we can make stronger inferences regarding the impact of
reappraisal interventions relative to a potentially useful strategy designed to equate
demand characteristics and expectancies. In the instructions, participants were
told that (emphasis in original) “One strategy that some people find helpful for
influencing their emotions is reflecting. This strategy involves allowing oneself to
freely experience and reflect on one’s thoughts and feelings. This strategy is based
on the insight that reflecting on your thoughts and feelings about any situation can
lead to different emotional responses. This means that exploring your thoughts
and emotions can change how you feel about the situation. For example, consider
someone who stays at home under lockdown due to COVID-19 and is feeling
anxious, sad, or angry. In this case, reflecting might involve allowing oneself to
experience these feelings and be fully immersed in the lockdown experience,
reflecting on the meaning this situation has for the person and their loved ones.”
Participants were then given four examples of how reflecting might be employed
for the COVID-19 situation (Example 1: “This situation is changing so fast, and I
don’t know how the future will develop.”; Example 2: “People are struggling to cope
with these unprecedented and overwhelming challenges.”; Example 3: “Someone I
love might get sick and there might not even be ventilators to help them.”;
Example 4: “I really wish we could find a vaccine soon.”).
To reinforce what they had learned, participants in the two reappraisal
conditions and the active control condition were then asked to summarize, in
one or two sentences, the strategy they had just learned. This text response was
collected only for exploratory purposes and was not used in confirmatory analysis.
In the passive control condition, participants received the following
instructions: “In this study, we will show you photographs related to COVID-19
from various news sources. Our goal is to better understand how people respond
to such photos, which may include feelings of fear, anger, and sadness. As you
view these photographs, please respond as you naturally would.” Having a passive
control condition allowed us to have clear interpretations in the case that we found
no significant difference in our contrast between both the reappraisal conditions
combined and both the control conditions combined. If this was the case, we would
have compared each reappraisal condition against the passive control condition
and compared the active control condition against the passive control condition in
the exploratory analysis to determine whether each strategy had a non-zero impact
relative to individuals’ natural responses.
Practice trials. After receiving instructions by condition, participants were asked
to practice the strategy in two trials designed to facilitate their understanding of
the strategy. The practice trials included providing ratings and written responses
to two photographs (per prior research108). The photographs in this study were
selected by our research team from major media news sources (CNN, New York
Times, The Guardian and Reuters) and present situations in Asia, Europe and
North America. They were rated by our team to evoke either sadness or anxiety
above the midpoint on a seven-point scale ranging from ‘not at all’ to ‘very’ and
to score close to or above the midpoint on a seven-point scale ranging from
‘not at all’ to ‘very’ on the question “How much do you recommend using this
picture?” (photographs available at https://osf.io/8bjnz/). In each practice trial,
participants saw a ‘negative’ photo related to the COVID-19 situation (for example,
an exhausted doctor or medical workers in hazmat suits) and a reminder above the
photo to use the strategy that was presented to them. In the reconstrual condition,
the reminder was “As you view the photo, draw on the examples we gave you earlier
in order to interpret the situation in a new way.” In the repurposing condition, the
reminder was “As you view the photo, draw on the examples we gave you earlier
in order to focus on any good you can find in the situation.” In the active control
condition, the reminder was “As you view the photo, draw on the examples we gave
you earlier in order to reflect on your thoughts and feelings.” In the passive control
condition, the reminder was “As you view the photo, respond as you naturally
would.” After 10 s, participants were asked to rate their emotions in response to the
photo using two corresponding unipolar five-point Likert scales, one for negative
emotion and one for positive emotion. These ratings were designed to familiarize
participants with the task, and were not used in the confirmatory analyses. After
each photo, participants in the two reappraisal conditions and the active control
condition were asked to write (in text) how they applied the strategy while
observing the photo. Participants in the passive control condition were asked to
write (in text) anything that comes naturally to their mind about the photo. The
text response was also collected only for exploratory purposes and was not used in
the confirmatory analysis. Participants in the two reappraisal conditions and the
active control condition were then given one example of how the photo might be
viewed (examples varied by condition). Note that the two reappraisal conditions
and the active control condition were designed to be matched for demand
characteristics and expectancy.
Experimental trials. Following the two practice trials, participants viewed
additional photos related to the COVID-19 situation in ten experimental trials.
Participants in the two reappraisal conditions and the active control condition
were asked to use the strategy that they practiced, and participants in the passive
control condition were asked to respond naturally. All participants saw exactly
the same ten photos, but the order of the presentation was randomized across the
ten experimental trials. Each photo was presented to participants with the same
reminder used in the practice trials. After observing each photo for ten seconds,
participants were asked to rate both their negative and positive emotions in
response to the photo using the same five-point Likert scales from the practice
trials.
Post-measures. In the final section of the study, participants completed several
measures, including (1) negative and positive state emotions, (2) negative and
positive emotions about the COVID-19 situation, (3) negative and positive
anticipated emotions, (4) behavioural intentions, (5) motivation/beliefs, and (6)
manipulation check.
Measures. Demographics. At the beginning of the study, participants completed a
general survey that included demographic questions and some questions related to
COVID-19 shared by all three studies in the PSA COVID-19 Rapid Project. Details
about the general survey can be found at https://osf.io/7axc4/. While we originally
planned for the general survey to appear at the end of the study, it was necessary
for recruitment purposes (selecting representative panels) that it appear at the
beginning of the study.
Baseline emotions. To assess baseline emotion, we asked participants how they
were feeling right now at the beginning of the session on a five-point scale
ranging from 1 (not at all) to 5 (extremely) (all response options were labelled
and numbers were not displayed to participants for clarity). For negative baseline
emotions, we measured five items on fear, anger, sadness, distrust and stress from
the modified differential emotions scale109. For positive baseline emotions, we
measured five items on hope, gratitude, love, inspiration and serenity from the
modified differential emotions scale109 (details for all scoring rules are described
in ‘Analysis plan’). We also measured three items on loneliness110 and three items
on social connectedness111. These six items also were included in the assessment of
post-photo state emotions and in the assessment of anticipated emotions (at each
assessment point, these six items were used in exploratory analyses).
Negative emotional responses. In order to capture descriptively rich, nuanced
data, we measured negative emotional responses in four ways. The first way is to
measure negative emotions in response to the photos. For each photo, we asked
participants how negative the photo made them feel using a unipolar scale ranging
from 1 (not at all) to 5 (extremely). The second way is to measure negative state
emotions after viewing all ten photos. We asked participants “how you are feeling
right now” with the same set of items used to measure baseline emotions, which
included five negative state emotions of fear, anger, sadness, distrust and stress.
The third way is to measure negative emotions about the COVID-19 situation. We
asked participants how negative/hopeless they were feeling about the COVID-19
situation right now on a unipolar scale ranging from 1 (not at all) to 5 (extremely).
The fourth way is to measure negative anticipated emotions, which were an
exploratory outcome. We asked participants “In the next week, to what extent, if
at all, do you think you will feel each of the following?” with the same set of items
used to measure baseline emotions, which included five negative anticipated
emotions of fear, anger, sadness, distrust and stress.
Positive emotional responses. Following a parallel procedure, we measured positive
emotional responses in four ways. The first way is to measure positive emotions
in response to the photos. For each photo, we asked participants how positive
the photo made them feel using a unipolar scale ranging from 1 (not at all) to 5
(extremely). The second way is to measure positive state emotions after viewing all
ten photos. We asked participants “how you are feeling right now” with the same
set of items used to measure baseline emotions, which included five positive state
emotions of hope, gratitude, love, inspiration, and serenity. The third way is to
measure positive emotions about the COVID-19 situation. We asked participants
how positive/hopeful they were feeling about the COVID-19 situation right now
on a unipolar scale ranging from 1 (not at all) to 5 (extremely). The fourth way is
to measure positive anticipated emotions, which were an exploratory outcome. We
asked participants “In the next week, to what extent, if at all, do you think you will
feel each of the following?” with the same set of items used to measure baseline
emotions, which included five positive anticipated emotions of hope, gratitude,
love, inspiration and serenity.
Behavioural intentions. In addition to the emotional responses that are central to
our four confirmatory hypotheses in this study, we also examined exploratory
outcomes concerning behavioural intentions. Such intentions matter because
they have been shown to predict actual behaviours112,113. Following protocols from
Fishbein and Ajzen114, we asked participants to indicate on a 7-point scale ranging
from 1 (extremely unlikely) to 7 (extremely likely) their intentions to engage in
each of 10 different behaviours within the next week. Five of the items concern
potentially harmful behaviour, which we chose based on documented links
between negative emotions and substance use, aggressive behaviour and excessive
information seeking17,25,115. Items included: drinking too much alcohol, using too
much tobacco (for example, smoking or vaping) or other recreational drugs, yelling
at someone, taking anger out online and spending too much time on media.
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The other five items concerned beneficial behaviour, which we chose based on
evidence that positive emotions contribute to more health behaviours84,85. Items
include: eating healthy food, getting enough physical activity, practicing healthy
sleep habits (for example, going to bed and waking at regular hours), washing
hands regularly for at least 20 s, and following a stay-at-home order stringently (if
there isn’t an order in your region now, assume that one is imposed).
Motivation and beliefs. We measured both the motivation to use the emotion
regulatory strategy and the belief in the effectiveness of the emotion regulatory
strategy as exploratory moderators71,87. We asked “Recall the instructions we gave
you for viewing the photos. To what extent, if at all, do you agree or disagree with
the following statements?” Motivation to use the emotion regulatory strategy was
measured with the item: “I tried my hardest to follow the instructions I was given
while viewing the photos.” Belief in the effectiveness of the emotion regulatory
strategy employed by participants was measured with the item “I believed that
following the instructions would influence my emotions.” Participants rated their
answers using a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly
agree).
Manipulation check. We planned to evaluate participants’ attention to our
instructions and photos using two multiple-choice questions. The first question
asked participants to choose the instructions they had at the beginning of the
survey from among four options. The second question asked participants to choose
the photo that was not shown to them in the survey from among three options.
For exploratory purposes, we also asked how often participants actually used
each approach when viewing the photographs and their global change of emotions
compared to the beginning of the study. Participants were asked, “When viewing
the ten photographs related to COVID-19 earlier, how often did you use each of
the following approaches?” and rated four approaches: “responding as I naturally
would,” “reflecting on my thoughts and feelings,” “interpreting the situation in a
new way,” and “focusing on any good I could find in the situation.” Participants
rated their answers using a 5-point scale ranging from 1 (never) to 5 (always). To
measure global change of emotion, participants were asked, “Overall, compared
to the beginning of this study, how negative do you feel right now?” using a
5-point scale ranging from 1 (much more negative) to 5 (much less negative) and
“Overall, compared to the beginning of this study, how positive do you feel right
now?” using a 5-point scale ranging from 1 (much more positive) to 5 (much less
positive).
Order of items. For measures above, items belonging to the negative category (that
is, negative emotional responses and intentions for harmful behaviour) and to the
positive category (that is, positive emotional responses and intentions for beneficial
behaviour) were presented in a counterbalanced order within each measure across
participants. In other words, half of the participants always rated an item from the
negative category first and then an item from the positive category, whereas the
other half always rated an item from the positive category first and then an item
from the negative category. For measures that have multiple items, items belonging
to the negative category were randomized within the negative category, and items
belonging to the positive category were randomized within the positive category.
When the same set of items used to measure baseline emotions was repeated, the
set had the same order for every given participant.
Analysis plan. Pre-processing. Exclusion. We planned to exclude (1) participants
who answered both multiple-choice manipulation check questions incorrectly, and
(2) participants who completed fewer than 50% of the questions in the study.
Reliability of measures. For items from the modified differential emotions scale109,
we planned to create overall negative emotion scores at each time point by
averaging the five negative emotions (fear, anger, sadness, distrust and stress) and
overall positive emotion scores at each time point by averaging the five positive
emotions (hope, gratitude, love, inspiration and serenity) if the average inter-item
correlation was above 0.40 for negative emotions and for positive emotions,
respectively. If the average inter-item correlation was below 0.40, we would
conduct an exploratory factor analysis with oblique rotation and maintain factors
with an eigenvalue above 1.00. If no factors had an eigenvalue above 1, we would
report results by item rather than as a composite. The actual average inter-item
correlation was 0.50 for negative baseline emotions and 0.48 for positive baseline
emotions. Therefore, we created overall negative emotion scores at each time point
by averaging the five negative emotions and overall positive emotion scores at each
time point by averaging the five positive emotions.
Missing data. We dropped incomplete cases on an analysis-by-analysis basis. Given
our sampling plan described below, we should have power of 0.95 or above.
Outliers. In order to be maximally conservative, we did not define or identify
outliers.
Analytic plan for hypotheses. Since negative emotional responses and positive
emotional responses are separable79,80, we examined negative emotional responses
and positive emotional responses separately. To control family-wise error rates in
multiple comparisons, we used the Holm–Bonferroni method within each of the
four hypotheses separately. For all analyses testing negative emotional responses
(hypothesis 1 and hypothesis 3), we planned to control for the participants’
negative baseline emotions. As originally intended by the scale109, we planned to
create an overall negative baseline emotion score by averaging the five negative
emotions (fear, anger, sadness, distrust and stress). For all analyses testing positive
emotional responses (hypothesis 2 and hypothesis 4), we planned to control for
the participants’ positive baseline emotions. As originally intended by the scale109,
we planned to create an overall positive baseline emotion score by averaging the
five positive emotions (hope, gratitude, love, inspiration and serenity). To account
for the nested structure in our data (for example, participant nested by country/
region), we fitted multilevel models with the condition using the contrast in Table
1, random by-country/region slopes, and random by-country/region intercepts.
If a model failed to converge, we planned to explore other reasonable models113
and report results of all explored models in an appendix. We visually assessed
assumptions of heteroscedasticity and normality of residuals and found no severe
deviations. All tests were two-tailed.
Although we used the frequentist approach for confirmatory analyses, we also
reported Bayes factors for every result to gain information about the strength of
evidence provided by the data comparing the null and alternative hypotheses116.
If we obtained non-significant results from the frequentist approach, we used
Bayes factors to help us interpret non-significant results and differentiate between
insensitive results and those that reveal good enough evidence supporting the null
hypothesis. We set these evidence thresholds to BF10 >10 for H1 and BF10 <0.1 for
H0. If Bayes factors did not cross the evidence thresholds, we think our sample
size is sufficiently large that inconclusive results at this sample size would be
an important message for the field. We used informed priors for the alternative
model: a one-tailed Cauchy distribution with a mode of zero and a scale r = 0.18
(hypotheses 1 and 2), r = 0.17 (hypothesis 3) and r = 0.25 (hypothesis 4) on the
standardized effect size. These priors were based on the lowest available estimates
of effect sizes in past research (more information in ‘Sampling plan’). At stage 1,
we wrote the code for the Bayesian part of our analysis plan using the BayesFactor
package117 in R. We also planned to investigate the sensitivity of our conclusions
to priors using robustness regions118, which involves calculating a Bayes factor
under a large number of different priors to see how the Bayes factor changes.
After we collected our data, we made the following adjustments to our plans for
our Bayesian analysis. First, to estimate the Bayesian models, we switched from
the BayesFactor package to the brms package119 because of its superior handling
of random effects. Our brms models used four chains, each with 1,000 warm-up
samples, 10,000 post-warm-up samples and a thinning rate of 1. To calculate
Bayes factors, we used bridge sampling, as implemented in the bayestestR120 and
bridgesampling121 packages, to compare the marginal likelihoods of the full model
versus a null model that does not contain one of our two focal contrasts. Second,
we discovered that the Bayesian versions of our models involving emotional
responses to the photos had high computational requirements due to the inclusion
of two sources of random effect (country/region and participant) rather than
one. To make these models more computationally manageable we simplified the
dataset by computing the average emotional response to each photo for each
participant and using this as the outcome variable. This allowed us to omit the
by-participant random effect in these models and drastically reduce the resource
requirements and compute time. Although these simplified models do not separate
participant-specific variance from error variance, our analysis plan had no plans
to interpret these sources of variation separately, so we reasoned this simplification
was a fair way to obtain the same mathematical results as required by our analysis
plan at a lower computational cost. Finally, we simplified the robustness analyses by
only investigating how the Bayes factors change with one very large prior (r = 1.0)
rather than computing full robustness regions. We made this last change to once
again reduce the compute time to manageable levels. If the Bayes factors under the
large prior are in line with those generated by the pre-registered priors (which are
already very small), the results should be robust to other reasonable priors.
Tests for hypotheses 1 and 3. Overall, we expected that reappraisal interventions
(versus control) would reduce negative emotional responses (hypothesis 1), and
that reconstrual would lead to greater decreases in negative emotional responses
than repurposing (hypothesis 3). We tested hypothesis 1 and hypothesis 3 using
two orthogonal contrasts (Table 1). The first contrast is between both reappraisal
conditions combined and both control conditions combined for hypothesis 1.
The second contrast is between the reconstrual condition and the repurposing
condition for hypothesis 3. Negative emotional responses were measured in four
ways (negative emotions in response to the photos, negative state emotions after
viewing the photos, negative emotions about the COVID-19 situation, and negative
anticipated emotions). We had confirmatory hypotheses regarding the first three
outcomes and examined negative anticipated emotions in the exploratory analysis.
Therefore, hypothesis 1 can be subdivided into hypotheses 1a to 1c, and hypothesis
3 can be subdivided into hypotheses 3a to 3c. We planned to consider a hypothesis
to be supported if at least 1 of the 3 sub-hypotheses is significant after Holm–
Bonferroni correction (controlling for 3 comparisons within each hypothesis).
If we found non-significant results for any sub-hypothesis, we compared each
reappraisal condition against the passive control condition and compared the active
control condition against the passive control condition in the exploratory analysis
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to determine whether each strategy had a non-zero impact relative to individuals’
natural responses.
Testing effects on negative emotions in response to the photos. We expected
that reappraisal interventions (versus control) would reduce negative emotions
in response to the photos (hypothesis 1a), and reconstrual would lead to greater
decreases in negative emotional responses in response to the photos than
repurposing (hypothesis 3a). We modelled ratings of negativity in response to each
photo in the experimental trials as a function of the fixed effects of condition using
our contrast. We included by-participant random intercepts, by-country/region
random intercepts, as well as by-country/region random slopes for each contrast.
Testing effects on negative state emotions. We expected that reappraisal
interventions (versus control) would reduce negative state emotions (hypothesis
1b) and reconstrual would lead to greater decreases in negative state emotions
than repurposing (hypothesis 3b). Similar to creating the overall negative baseline
emotion score, we planned to create an overall negative state emotion score by
averaging the five negative emotions (fear, anger, sadness, distrust and stress). We
modelled the overall negative state emotion score as a function of the fixed effects
of condition using our contrast. We included by-country/region random intercepts,
as well as by-country/region random slopes for each contrast.
Testing effects on negative emotions about the COVID-19 situation. We expected
that reappraisal interventions (versus control) would reduce negative emotions
about the COVID-19 situation (hypothesis 1c), and reconstrual would lead
to greater decreases in negative emotions about the COVID-19 situation than
repurposing (hypothesis 3c). We modelled negative emotions about the COVID-
19 situation as a function of the fixed effects of condition using our contrast.
We included by-country/region random intercepts, as well as by-country/region
random slopes for each contrast.
Tests for hypotheses 2 and 4. Overall, we expected that reappraisal interventions
(versus control) would increase positive emotional responses (hypothesis 2),
and repurposing would lead to greater increases in positive emotional responses
than reconstrual (hypothesis 4). We tested hypothesis 2 and hypothesis 4 using
two orthogonal contrasts (Table 1). The first contrast is between both reappraisal
conditions combined and both control conditions combined for hypothesis 2.
The second contrast is between the reconstrual condition and the repurposing
condition for hypothesis 4. Positive emotional responses were measured in four
ways (positive emotions in response to the photos, positive state emotions after
viewing the photos, positive emotions about the COVID-19 situation, and positive
anticipated emotions). We had confirmatory hypotheses regarding the first three
outcomes and examined positive anticipated emotions in an exploratory analysis.
Therefore, hypothesis 2 can be subdivided into hypotheses 2a to 2c, and hypothesis
4 can be subdivided into hypotheses 4a to 4c. We planned to consider a hypothesis
to be supported if at least 1 of the 3 sub-hypotheses is significant after Holm–
Bonferroni correction (controlling for 3 comparisons within each hypothesis). If
we found non-significant results for any sub-hypothesis, we would compare each
reappraisal condition against the passive control condition and compare the active
control condition against the passive control condition in the exploratory analysis
to determine whether each strategy had a non-zero impact relative to individuals’
natural responses.
Testing effects on positive emotions in response to the photos. We expected
that reappraisal interventions (versus control) would increase positive emotions
in response to the photos (hypothesis 2a), and that repurposing would lead to
greater increases in positive emotions in response to the photos than reconstrual
(hypothesis 4a). We modelled ratings of positivity in response to each photo in the
experimental trials as a function of the fixed effects of condition using our contrast.
We included by-participant random intercepts, by-country/region random
intercepts, as well as by-country/region random slopes for each contrast.
Testing effects on positive state emotions. We expected that reappraisal
interventions (versus control) would increase positive state emotions (hypothesis
2b), and repurposing would lead to greater increases in positive state emotions in
response to the photos than reconstrual (hypothesis 4b). Similar to creating the
overall positive baseline emotion score, we planned to create an overall positive
state emotion score by averaging the five positive emotions (hope, gratitude, love,
inspiration and serenity). We modelled the overall positive state emotion score
as a function of the fixed effects of condition using our contrast. We planned to
include by-country/region random intercepts, as well as by-country/region random
slopes for each contrast. However, the model could not converge when we included
by-country/region random slopes for contrast 2. To make the model converge, we
did not include by-country/region random slopes for contrast 2.
Testing effects on positive emotions about the COVID-19 situation. We expected
that reappraisal interventions (versus control) would increase positive emotions
about the COVID-19 situation (hypothesis 2c), and repurposing would lead
to greater increases in positive emotions about the COVID-19 situation than
reconstrual (hypothesis 4c). We modelled positive emotions about the COVID-19
situation as a function of the fixed effects of condition using our contrast. We
included by-country/region random intercepts, as well as by-country/region
random slopes for each contrast.
Exploratory analyses. We conducted a series of exploratory analyses to address
supplemental questions regarding our hypotheses, including, but not limited to:
(1) Were there any differences in other pairwise comparisons in testing hypotheses
1–2? (2) Were there emotion-specific effects of reappraisal122? (3) Were the
effects on emotions subjectively detectable by participants123? Did the effects of
strategy use vary by (4) motivation to use the strategy71; (5) beliefs in the strategy’s
effectiveness87; or (6) the participant’s country of residence90?
We investigated the impacts of strategy use on other outcomes, including, but
not limited to: (1) positive and negative anticipated emotions; (2) intentions to
enact potentially harmful versus beneficial behaviours (results in Supplementary
Table 14); and (3) loneliness and social connectedness (results in Supplementary
Table 15).
Sampling plan. Expected eect sizes. In order to compare eect sizes across studies,
below we report values of Cohen’s d, which in some cases were transformed or
calculated from the results reported in the original studies (see Supplementary
Table 16 for details). Several caveats are in order regarding the eect sizes that
follow. First, meta-analyses tend to overestimate eect sizes, although the size
of overestimation varies considerably across studies and sometimes shows no
overestimation124. Second, most previous studies were conducted in the laboratory,
whereas the current study was conducted online. ird, the current crisis is likely
to lead to strong emotional responses, especially for participants who are facing
nancial or health-related setbacks, although strong negative emotions also
motivate people to regulate emotions more64. ese caveats suggest uncertainty in
eect sizes.
In general, reappraisal has an average effect size of d = 0.45, 95% CI = [0.35,
0.56] in changing emotion experience relative to passive control conditions (that
is, no instruction, instructions to experience naturally, instructions to not regulate
in a certain manner, or instructions to enhance or maintain the focal emotion) (a
meta-analysis39 finds no evidence of publication bias). Experimental disclosure
and expressive writing, which inspired the instruction in the active control
condition, have an average effect size of d = 0.07, 95% CI = [0.05, 0.17] in improving
psychological health (including emotional responses), relative to engaging in
non-treatment neutral activities (for example, describing what they have done in
the past 24 h) or no activities (a meta-analysis77 finds evidence of publication bias).
These works suggest the lowest available estimate of the effect size to be d = 0.18
(subtracting the upper bound of 95% CI d = 0.17 for experimental disclosure and
expressive writing from the lower bound of 95% CI of d = 0.35 for the reappraisal
interventions) between our reappraisal interventions and the control conditions for
hypothesis 1 and hypothesis 2.
In relation to the comparison between reconstrual and repurposing, although
prior research has not used the same theoretical framework76 to empirically
contrast reconstrual and repurposing as we did in the current study, research
on closely related constructs can provide estimates of effect sizes. Reconstrual is
most similar to a previously studied subtype of reappraisal called ‘reappraising
emotional stimulus’ in Webb, Miles and Sheeran’s meta-analysis39, which has a
d = 0.38, 95% CI = [0.21, 0.55] in changing emotion experience (this effect size is
primarily for negative emotions, as all but one study examined negative emotions).
Repurposing is similar to the construct ‘benefit finding’ (perceiving positive
consequences that resulted from a traumatic event), which is associated with
positive well-being, d = 0.45, 95% CI = [0.37, 0.52], but not global distress,
d = 0.00, 95% CI = [−0.04, 0.04] (meta-analysis81). Repurposing is also similar
to the subtype of reappraisal called ‘positive reappraisal’, which is more effective
in increasing positive thoughts than other types of reappraisals, d = 0.49,
95% CI = [0.25, 0.72] relative to detached reappraisal125. These works suggest the
lowest available estimate of the effect size to be d = 0.17 (subtracting the upper
bound of 95% CI d = 0.04 for the association between benefit finding and
global distress from the lower bound of 95% CI of d = 0.21 for ‘reappraising
emotional stimulus’39 between reconstrual and repurposing in changing
negative emotions for hypothesis 3), and d = 0.25 (the lower bound of 95% CI
of positive reappraisal in increasing positive thoughts than detached reappraisal125)
between reconstrual and repurposing in changing positive emotions for
hypothesis 4.
Sample size. For practical reasons, sample size was decided primarily on the basis of
the availability of resources among members of the PSA.
Adjusted alpha levels. The tests of each hypothesis involved three comparisons, with
α for the smallest P value being 0.017 (that is, 0.05/3), α for the second-smallest P
value being 0.025 (that is, 0.05/2), and α for the largest P value being 0.05 (Holm–
Bonferroni corrections).
Power analysis. We conducted a simulation study to estimate power for a variety of
potential effect sizes (|d| = 0.05 to 0.29, separated by increments of 0.02), number
of countries/regions (Ncountry/region = 30, 35, 40, 45, 50, 55, 60), within-country/
region sample sizes (N = 200, 400, 600, 800), by-country/region intercept
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variances (σ2intercept = 0.05, 0.30, 0.55, 0.80), and by-country/region slope variances
(σ2slope = 0.0, 0.02, 0.03, 0.04) at α = .017. The lowest level of intercept variances
in our simulation was chosen on the basis of an ongoing multi-country/region
project tracking rates of depression (σ2intercept = 0.04) and worries about the COVID-
19 (σ2intercept = 0.06) across countries/regions during the COVID-19 outbreak126
(details in Supplementary Table 16). The lowest level of slope variances in our
simulation was chosen on the basis of the average slope variance (σ2slope < 0.01)
in a large multi-site, multi-country/region project involving 28 psychological
manipulations127. The slope variances capture the variability of the effect of
psychological manipulations, and there is no apparent reason to expect that the
effect of reappraisal interventions on emotions is more variable than most other
psychological manipulations reported in Klein et al.127. In fact, appraisal theories of
emotion argue that the relationship between appraisals and emotions is culturally
universal128, suggesting low variability. As one example to show that similar
appraisals associate with similar emotional experiences, we found the associations
varied little across countries/regions between perceived insufficient government
response and depression (σ2slope = 0.003) and between perceived insufficient
government response and worries (σ2slope = 0.003) during the COVID-19
pandemic126 (details in Supplementary Table 16), consistent with the observation
of low slope variances (σ2slope < 0.01) in Klein et al. 127. Despite expecting low
variability from empirical findings and theories, we tested a variety of intercept
variances and slope variances in our power simulation, some of which were
much higher than those reporrted in refs. 127,126 to be maximally conservative.
We conducted 1,000 simulations for each set of simulation parameters using the
simr package129 using computing power harnessed through the Open Science
Grid130,131.
We show comprehensive results for our simulation study at https://osf.io/
mf5z4/. In our final sample after pre-registered exclusion, 37 countries/regions had
over 200 participants, surpassing the 95% power criterion based on simulations.
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
The analytic dataset is available at https://osf.io/jeu73/. Materials are available at
https://osf.io/4yf9d/, with additional relevant materials for the PSA’s rapid-response
COVID-19 projects at https://osf.io/s4hj2/.
Code availability
All analysis code (completed in R) is available at https://osf.io/jeu73/.
Received: 17 April 2020; Accepted: 28 June 2021;
Published online: 2 August 2021
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Acknowledgements
This project was supported by funds from: the Amazon Web Services (AWS) Imagine
Grant (to E.M.B.); the Japan Society for the Promotion of Science Grants-in-Aid for
Scientific Research (JSPS KAKENHI; 16h03079, 17h00875, 18k12015, and 20h04581
to Y.Y.); the research programme Dipartimenti di Eccellenza from the Ministry of
Education University and Research (MIUR to N. Cellini and G.M. and the Department
of General Psychology of the University of Padua); statutory funds of the University of
Wroclaw (to A. Sorokowska); the Charles University Research Programme PROGRES
(Q18 to M. Vranka); the Knut and Alice Wallenberg Foundation (2016:0229 to J.K.O.);
the Rubicon Grant (019.183sg.007 to K.v.S.) from the Netherlands Organisation for
Scientific Research; the Australian Research Council (dp180102384 to R.M.R.); the US
National Institutes of Health (NIMH111640 to M.N.-D.), the Huo Family Foundation
to N.J.; the NSF Directorate for Social, Behavioral and Economic Sciences, Division
of Social and Economic Sciences (1559511 to J.S.L.); the US National Institutes of
Health (RO1-CA-224545 to J.S.L.); Eesti Teadusagentuur–Estonian Research Council
(PSG525 to A. Uusberg); the J. William Fulbright Program (to F. Azevedo); the HSE
Basic Research Program (to D. Dubrov); Dominican University (a Faculty Development
Grant to A. Krafnick); and the French National Research Agency Investissements d’avenir
supporting PSF (ANR-15-IDEX-02 to H.I.); the Slovak Research and Development
Agency (project no. APVV-20-0319 to M. Adamkovič); the programme FUTURE
LEADER of Lorraine Université d’Excellence within the French National Research
Agency Investissements d’avenir (ANR-15-IDEX-04-LUE to S.M.). Computation for this
research was assisted by: the Harvard Business School compute cluster (HBSGrid); and
the Open Science Grid. The Open Science Grid is supported by the National Science
Foundation award 1148698 and the US Department of Energy’s Office of Science,
as well as by the compute resources and assistance of the UW-Madison Center For
High Throughput Computing (CHTC) in the Department of Computer Sciences.
The CHTC is supported by UW-Madison, the Advanced Computing Initiative, the
Wisconsin Alumni Research Foundation, the Wisconsin Institutes for Discovery, and
the National Science Foundation, and is an active member of the Open Science Grid,
which is supported by the National Science Foundation and the U.S. Department of
Energy’s Office of Science. We thank data science specialist S. Worthington and the
research computing environment at the Institute for Quantitative Social Science, Harvard
University and V. Ivanchuk for research assistantship. Our semi-representative panels
were made possible by an in-kind purchase from the Leibniz Institute for Psychology
(protocol https://doi.org/10.23668/psycharchives.3012); a special grant from the
Association for Psychological Science and a fee waiver from Prolific. This work was
supported by a grant from the American Psychological Society (granted to the PSA).
Further financial support was provided by the PSA and a special crowdfunding campaign
initiated by the PSA. We thank Amazon Web Services for help with server needs, the
Leibniz Institute for Psychology (ZPID) for help with data collection via the organization
and implementation of semi-representative panels, Prolific Inc. for offering discounted
recruitment, and Harvard University’s Institute for Quantitative Social Sciences for
statistical consulting. Finally, this research was supported by resources from the Open
Science Grid, which is supported by National Science Foundation award 1148698,
and the U.S. Department of Energy’s Office of Science. Beyond those roles already
acknowledged, the funders had no role in study design, data collection and analysis,
decision to publish or preparation of the manuscript.
Author contributions
Conceptualization: K. Wang, A. Goldenberg, C.A.D., A. Uusberg, J.S.L. and J.J.G. Data
curation: E.M.B. and P.S.F. Formal analysis: K. Wang, P.S.F. and B. Palfi. Funding
acquisition: C.R.C., E.M.B., P.S.F. and H.I. Investigation: J.K.M., L.E., D.H.O., E.A.J.,
E.O.L.G., J.P.W., K. Desai, E.K., M. Pantazi, N. Pilecka, G.M.M., E.A., M. Adamkovič,
M. Roczniewska, C. Reyna, A.P.K., M. Westerlund, L.A., S.P., A.I.A., N.C.A., C.E.O.,
I.L.G.N., I. Dalgar, P.M.M., F.F., M. Willis, A.C.S., A. Mokady, N.R., M.R.V., N.L.N.,
M. Parzuchowski, M.F.E.B., M. Vranka, M.B.K., I.R., M. Harutyunyan, E.Y., M. B ecker,
E. Manunta, G.K., D. Marko, K.E., D.M.G.L., A. Findor, K.P., A.T.L., J.J.B.A., M.S. Ortiz,
Z.V., E.P., M. Voracek, C.L., M.G., J.V.V., G.M., N. Cellini, S.-C.C., J.Z., K.M., N.L.,
A. Karababa, L. Boucher, W.M.C., J. Bavolar, R.M.R., I.D.S., T.J.H., S. Azouaghe, R.M.,
C.G., C.S.S., G.A., W.J.-L., M. Bradford, L.C.P., J.E.C.V., J.C.V.N., A. Arvanitis, Q.X., R.C.,
S.Z., Z. Tajchman, I.V., J.M.P., J.R.K., M. Atari, M. Hricova, P.K., J.S., R.-M.R., S.F.M.,
I. Zakharov, M. A. Koehn, C.E.-S., R.J.C.-J., A.J.K., E.Š., J. Urban, J.R .S., M. Martončik,
S.B.O., D. Šakan, A.O.K., J.M.D., I.A.T.A., A. Ferreira, L.B.L., H. Manley, D.Z.R., R.P.M.,
NATURE HUMAN BEHAVIOUR | VOL 5 | AUGUST 2021 | 1089–1110 | www.nature.com/nathumbehav 1103
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E. Musser, W.C., H.G., S.R.-F., C. Reeck, C. Batres, D.S.A., M.M.B., Z.C., F.V., I. Ziano,
M. Tümer, A.C.A.C., D. Dubrov, M.d.C.M.C.T.R., C.A., A. Sacakli, C.D.C., K.L.R., G.S.,
J.T.P., T.B., H.D., M. Hruška, D. Sousa, K.B., A.N.Z., M.P.-C., M. Bialek, M. Kowal,
A. Sorokowska, M. Misiak, D. Mola, M.V.O., P.S.C., A. Belaus, P.A., R.O., L.A.V.,
P. Szwed, M. Kossowska, J. Kielińska, B. Antazo, G.N., N. Simonovic, J.T., A.G.-K., A.D.,
K.I., A. Urooj, T.G., A.A., N.A.-A., H.B.K., B.G., T.H., M.V.J., J.B.B., W.J.C., S.Ç.,
M. Seehuus, A. Khaoudi, A. Bokkour, K.A.E.A., I. Djamai, A. Iyer, N. Parashar, A.
Adiguzel, H.E.K., C.B., J.O.N., M.P.-P., A.d.l.R.-G., V.A., N.B., D.G., A. Ivanov, I.P., M.
Romanova, I.S., M. Terskova, E.H., A.J., V.S., T.E.S., A.D.A., N.O., N. Say, M. Khosla,
A.G.T., F.Y.H.K., G. Bijlstra, F. Mosannenzadeh, B.B.B., U.-D.R., E. Baskin, J.C.-C., B.J.W.D.,
D. Moreau, C.A.M.S., C.N., H.F., M. Anne, S.M.J.J., N.M.M., Y.K., K.Y., S.D., A.H.,
M. Vdovic, P.A.G.F., J. Kamburidis, E. Marinova, M.N.-D., N.R.R., A. Stoyanova, K.S.,
S. Lins, I.R.P., M.K.-T., T.J., J.K.O., O.B., M. Marszalek, S.T., R.A., W.L., J.A., N.V.D., J.A.S.,
R.S., J. Miranda, K. Damnjanović, S.K.Y., B. Jaeger, D.R., G.P., K. Klevjer, N.S.C.-F.,
M.F.-A., M.Y.L., A.O.T., M. Toro, L.G.J.D., D.L.G.J.D., S.A.S., R.V., S.M., T.F., A. Bran,
D.C.V., L. Vieira, G.L.d.H.C., A. Greenburgh, C.M.W., A.M.T., L. Volz, C. Karaarslan,
E.S., T.B.A., M.F.C., T.J.S.L., M.F.F.R., M. Karekla, C. Karashiali, N. Sunami, L.M.J.,
D. Storage, A. Studzinska, P.H.P.H., D.L.H., M. Sirota, K. Wolfe, F.C., A.T., E.R.A., Y.L.,
E.C.W., H. Brohmer, G.H., O.D., K.V., G.F., G.A.T., A. Ahmed, M.L., N.T., H. Bai,
M. Manavalan, X.S., R.B.W., P.Z., A.D.R., L. Kozma, P.B., G. Banik, M.A.C.V., J. Uttley,
B.B., S.N.G., J.K.V., U.S.T., M.C.M., P. Sorokowski, A.G.-B., T.R., J.C., A.A.Ö., J.A.J.-G.,
M.V.H., T.I., A.L.W., J.P.R ., T.O., W.E.D., H.L.U., E.M.B., M.A.P., H.I. and P.S.F.
Methodology: K. Wang, A. Goldenberg, C.A.D., A. Uusberg, J.S.L., J.J.G., I.R., F.A.,
B. Aczel, P. Arriaga, A.G.T., M.A.S., M.C.M., H.L.U., D.M.B.-B. and P.S.F. Project
administration: J.K.M., C.Z., S.M.D., M.O., A. Szabelska, G.M.M., A.P.K., I. Dalgar, S.L.,
N.R., M.R.V., M. Vranka, M. Becker, G.K., E.P., N. Cellini, H. Azab, J.L.B., A.L.T., V.K.,
M.H.S., E.Š., M. Martončik, D. Dunleavy, K. Kirgizova, F.A., B. Palfi, M.A.M., I.L.P., B.
Aczel, P.A., A.G.-K., Y.Y., A. Urooj, L. Bylinina, A.A., B.G., A.D.A., C.A.L., B.I., H.C.-P.,
J.W.S., J.A., B. Paris, L. Volz, T.B.A., P.H.P.H., F.C., A. Ahmed, L. Kozma, J.E.B., K.K.T.,
M.A.S., S.M.I., C.R.E., C.R.C., P.R.M., H.L.U., E.M.B., N.A.C., M.A.P., D.M.B.-B., H.I.,
P.S.F. and H. Moshontz. Resources: B.B.A., M. Bernardo, O.C., K.G., S.M.D., A.P.J., K.R.,
M. Antoniadi, Z.G., E.K., K.N., O.N.B., M.O., M. Pantazi, N. Pilecka, A. Szabelska,
I.M.M.v.S., K.F., A.I.B., G.M.M., M. Adamkovič, M. Roczniewska, A.P.K., M. Westerlund,
L.A., S.P., G.A.A., P.D., I. Dalgar, H. Akkas, S.L., I.M.-O., A.C.S., A. Mokady, N.R., M.A.
Kurfali, M.R.V., M. Parzuchowski, M. Vranka, I.R., M. Harutyunyan, C. Wang, E.Y.,
M. Becker, E. Manunta, G.K., D. Marko, A. Findor, A.T.L., J.J.B.A., E.P., R.L., G.M.,
N. Cellini, S.-C.C., J.Z., H. Azab, A. Karababa, J.L.B., A.L.T., K.v.S., J.V., J. Bavolar,
L. Kaliska, V.K., L. Samojlenko, R.P., S.J.G., J. Beitner, L. Warmelink, S. Azouaghe,
A. Szala, C.G., C.S.S., O.J.G.-C., J.C.V.N., O.K., J.R.K., C.K.T., C.C.v.B., M.H.S.,
P.K., J.S., N. Cohen, M.Z., I. Zakharov, E.Š., D. Šakan, J.M.D., D.Z.R., R.P.M.,
D. Dunleavy, S.R.-F., K. Kirgizova, A. Muminov, F.A., D.S.A., J.M.L., Z.C., M. Tümer,
D. Dubrov, M.A.M., B.H., A. Sacakli, W.C.-L., M. Fedotov, M. Wielgus, I.L.P., M. Hruška,
B. Aczel, B.S., S. Adamus, K.B., L.M., N.-D.S., A.N.Z., M.P.-C., M. Bialek, M. Kowal,
F. Muchembled, R.R.R., P.A., R.O., M. Kossowska, G. C., J. Kielińska, B. Antazo, R.B.,
S. Stieger, G.N., A.G.-K., A.D., K.I., Y.Y., M. Čadek, J. Messerschmidt, M. Kurfalı,
A.A., E. Baklanova, B.G., J.B.B., S.Ç., A. Khaoudi, A. Bokkour, K.A.E.A., I. Djamai,
A. Adiguzel, H.E.K., N.B., E.H., V.H.K., V.S., T.E.S., A.D.A., L.M.S.P., D. Krupić, C.A.L.,
N.J., N. Say, S. Sinkolova, K.J., M. Stojanovska, D. Stojanovska, F. Mosannenzadeh,
U.-D.R., B.I., J.C.-C., H.C.-P., M. Topor, Y.K., M. Vdovic, L.A.-B., J. Kamburidis,
E. Marinova, M.N.-D., N.R.R., A. Stoyanova, M.K.-T., T.J., O.B., M. Marszalek, W.L., J.A.,
B.Ž., D. Krupić, K.H., K. Klevjer, D.V., R.V., S.M., A. Bran, L. Vieira, B. Paris, M. Capizzi,
G.L.d.H.C., X.D., L. Volz, M.J.B., C. Karaarslan, E.S., T.B.A., M. Korbmacher, J.P.H.V.,
N. Sunami, S.H., A. Studzinska, P.H.P.H., F.C., O.D., K.V., G.A.T., A. Ahmed, J. Bosch,
M. Friedemann, A.D.R., L. Kozma, S.G.A., R.C.C., G. Banik, L.M.R.-B., J.E.B., K.K.T.,
M.A.S., P.L.G.M., S.M.I., A.G.-B., V.C.A., T.I., L. Suter, M. Bernardo and E.M.B.
Supervision: J.K.M., A.L.T., M.H.S., J.W.S., K.K.T., C.R.E., C.R.C., P.R.M., H.L.U., E.M.B.,
N.A.C., M.A.P., D.M.B.-B., H.I., P.S.F. and H. Moshontz. Visualization: K. Wang,
A. Uusberg, A. Goldenberg, C.A.D., J.S.L. and J.J.G. Writing, original draft: K. Wang.
Writing, review and editing: K. Wang, A. Goldenberg, C.A.D., A. Uusberg, J.S.L., J.J.G.,
A. Uusberg, J.K.M., C.Z., B.B.A., M. Bernardo, O.C., L.E., K.G., D.H.O., E.A.J., E.O.L.G.,
S.M.D., A.P.J., K.R., J.P.W., M. Antoniadi, K. Desai, Z.G., E.K., K.N., O.N.B., M.O.,
M. Pantazi, N. Pilecka, A. Szabelska, I.M.M.v.S., K.F., A.I.B., G.M.M., E.A.,
M. Adamkovič, M. Roczniewska, C. Reyna, A.P.K., M. Westerlund, L.A., S.P., G.A.A.,
P.D., A.I.A., N.C.A., C.E.O., I.L.G.N., I. Dalgar, H. Akkas, P.M.M., S.L., I.M.-O., F.F., M.
Willis, A.C.S., A. Mokady, N.R., M.A. Kurfali, M.R.V., N.L.N., M. Parzuchowski,
M.F.E.B., M. Vranka, M.B.K., I.R., M. Harutyunyan, C. Wang, E.Y., M. Becker,
E. Manunta, G.K., D. Marko, K.E., D.M.G.L., A. Findor, K.P., A.T.L., J.J.B.A., M.S. Ortiz,
Z.V., E.P., M. Voracek, C.L., M.G., R.L., J.V.V., G.M., N. Cellini, S.-C.C., J.Z., K.M.,
H. Azab, N.L., A. Karababa, J.L.B., L. B oucher, W.M.C., A.L.T., K.v.S., J.V., J. Bavolar,
L. Kaliska, V.K., L. Samojlenko, R.P., S.J.G., J. Beitner, L. Warmelink, R.M.R., I.D.S.,
T.J.H., S. Azouaghe, R.M., A. Szala, C.G., C.S.S., G.A., W.J.-L., M. Bradford, L.C.P.,
J.E.C.V., O.J.G.-C., J.C.V.N., O.K., A. Arvanitis, Q.X., R.C., S.Z., Z.T., I.V., J.M.P., J.R.K.,
C.K.T., C.C.v.B., M. Atari, M.H.S., M. Hricova, P.K., J.S., R.-M.R., N. Cohen, S.F.M., M.Z.,
I. Zakharov, M.A. Koehn, C.E.-S., R.J.C.-J., A.J.K., E.Š., P.M.I., J. Urban, J.R.S.,
M. Martončik, S.B.O., D. Šakan, A.O.K., J.M.D., I.A.T.A., A. Ferreira, L.B.L., H. Manley,
D.Z.R., R.P.M., E. Musser, D. Dunleavy, W.C., H.G., S.R.-F., C. Reeck, C. Batres,
K. Kirgizova, A. Muminov, F.A., D.S.A., M.M.B., J.M.L., Z.C., F.V., I. Ziano, M. Tümer,
A.C.A.C., D. Dubrov, M.d.C.M.C.T.R., C.A., B. Palfi, M.A.M., B.H., A. Sacakli, C.D.C.,
K.L.R., G. S., J.T.P., T.B., W.C.-L., M. Fedotov, H.D., M. Wielgus, I.L.P., M. Hruška,
D. Sousa, B. Aczel, B.S., S. Adamus, K.B., L.M., N.-D.S., A.N.Z., M.P.-C., M. Bialek,
M. Kowal, A. Sorokowska, M. Misiak, D. Mola, M.V.O., P.S.C., A. Belaus, F. Muchembled,
R.R.R., P.A., R.O., L.A.V., P. Szwed, M. Kossowska, G.C., J. Kielińska, B. Antazo, R.B.,
S. Stieger, G.N., N. Simonovic, J.T., A.G.-K., A.D., K.I., Y.Y., A. Urooj, T.G., M. Čadek,
L. Bylinina, J. Messerschmidt, M. Kurfalı, A.A., E. Baklanova, N.A.-A., H.B.K., B.G., T.H.,
M.V.J., J.B.B., W.J.C., S.Ç., M. Seehuus, A. Khaoudi, A. Bokkour, K.A.E.A., I. Djamai,
A. Iyer, N. Parashar, A. Adiguzel, H.E.K., C.B., J.O.N., M.P.-P., A.d.l.R.-G., V.A., N.B.,
D.G., A. Ivanov, I.P., M. Romanova, I.S., M. Terskova, E.H., V.H.K., A.J., V.S., T.E.S.,
A.D.A., L.M.S.P., D. Krupić, C.A.L., N.J., N.O., N. Say, S. Sinkolova, K.J., M. Stojanovska,
D. Stojanovska, M. Khosla, A.G.T., F.Y.H.K., G. Bijlstra, F. Mosannenzadeh, B.B.B.,
U.-D.R., E. Baskin, B.I., J.C.-C., B.J.W.D., D. Moreau, C.A.M.S., H.C.-P., C.N., H.F., M.
Anne, S.M.J.J., M. Topor, N.M.M., Y.K., K.Y., S.D., A.H., M. Vdovic, L.A.-B., P.A.G.F.,
J. Kamburidis, E. Marinova, M.N.-D., N.R.R., A. Stoyanova, K.S., J.W.S., M.K.-T., T.J.,
J.K.O., O.B., M. Marszalek, S.T., R.A., W.L., J.A., B.Ž., N.V.D., J.A.S., R.S., J. Miranda,
K. Damnjanović, S.K.Y., D. Krupić, K.H., B. Jaeger, D.R., G.P., K. Klevjer, N.S.C.-F.,
M.F.-A., M.Y.L., A.O.T., M. Toro, L.G.J.D., D.V., S.A.S., R.V., S.M., T.F., A. Bran, D.C.V.,
L. Vieira, B. Paris, M. Capizzi, G.L.d.H.C., A. Greenburgh, C.M.W., A.M.T., X.D., L. Volz,
M.J.B., C. Karaarslan, E.S., T.B.A., M. Korbmacher, M.F.C., T.J.S.L., M.F.F.R., J.P.H.V., M.
Karekla, C. Karashiali, N. Sunami, L.M.J., D. Storage, S.H., A. Studzinska, P.H.P.H.,
D.L.H., M. Sirota, K. Wolfe, F.C., A.T., E.R.A., Y.L., E.C.W., H. Brohmer, G.H., O.D., K.V.,
G.F., G.A.T., A. Ahmed, M.L., J. Bosch, N.T., H. Bai, M. Manavalan, X.S., R.B.W., P.Z., M.
Friedemann, A.D.R., L. Kozma, S.G.A., S. Lins, I.R.P., R.C.C., P.B., G. Banik, L.M.R.-B.,
M.A.C.V., J. Uttley, J.E.B., K.K.T., B.B., S.N.G., M.A.S., P.L.G.M., J.K.V., U.S.T., S.M.I.,
M.C.M., P. Sorokowski, A.G.-B., T. Radtke, V.C.A., J.C., A.A.Ö., J.A.J.-G., M.V.H., T.I.,
A.L.W., J.P.R., T.O., W.E.D., L. Suter, M. Bernardo, C.R.E., C.R.C., P.R.M., H.L.U., E.M.B.,
N.A.C., M.A.P., D.M.B.-B., H.I., P.S.F. and H. Moshontz.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41562-021-01173-x.
Correspondence and requests for materials should be addressed to J.K.M.
Peer review information Nature Human Behaviour thanks Elaine Fox, David Mellor,
Thomas L. Webb and the other, anonymous, reviewer(s) for their contribution to the
peer review of this work. Peer reviewer reports are available.
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NATURE HUMAN BEHAVIOUR | VOL 5 | AUGUST 2021 | 1089–1110 | www.nature.com/nathumbehav
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Nature HumaN BeHaviour
Ke Wang 1, Amit Goldenberg2, Charles A. Dorison 3, Jeremy K. Miller 4 ✉ , Andero Uusberg 5,
Jennifer S. Lerner 6, James J. Gross 7, Bamikole Bamikole Agesin8, Márcia Bernardo9,
Olatz Campos10, Luis Eudave 11, Karolina Grzech 12,13, Daphna Hausman Ozery14, Emily A. Jackson15,
Elkin Oswaldo Luis Garcia 16, Shira Meir Drexler 17, Anita Penić Jurković18, Kafeel Rana 19,
John Paul Wilson 20, Maria Antoniadi 21, Kermeka Desai22, Zoi Gialitaki 23, Elizaveta Kushnir24,
Khaoula Nadif25, Olalla Niño Bravo26, Rafia Nauman27, Marlies Oosterlinck28, Myrto Pantazi 29,
Natalia Pilecka30, Anna Szabelska 31, I. M. M. van Steenkiste32, Katarzyna Filip33,
Andreea Ioana Bozdoc34, Gabriela Mariana Marcu 34,35, Elena Agadullina36, Matúš Adamkovič 37,38 ,
Marta Roczniewska 39,40, Cecilia Reyna 41, Angelos P. Kassianos 42,43, Minja Westerlund 44,
Lina Ahlgren45, Sara Pöntinen45, Gabriel Agboola Adetula46, Pinar Dursun 47,
Azuka Ikechukwu Arinze 48, Nwadiogo Chisom Arinze 48, Chisom Esther Ogbonnaya 48,
Izuchukwu L. G. Ndukaihe 48, Ilker Dalgar 49, Handan Akkas 50, Paulo Manuel Macapagal 51,
Savannah Lewis 52, Irem Metin-Orta 53, Francesco Foroni 54, Megan Willis 55,
Anabela Caetano Santos 56,57,58, Aviv Mokady 59, Niv Reggev 60, Merve A. Kurfali61,
Martin R. Vasilev 62, Nora L. Nock 63, Michal Parzuchowski 64, Mauricio F. Espinoza Barría 65,
Marek Vranka 66, Markéta Braun Kohlová 67, Ivan Ropovik 68,69, Mikayel Harutyunyan 70,
Chunhui Wang 71, Elvin Yao 72, Maja Becker 73, Efisio Manunta 73, Gwenael Kaminski 73,
Dafne Marko74, Kortnee Evans75, David M. G. Lewis 75,76, Andrej Findor 77, Anais Thibault Landry78,
John Jamir Benzon Aruta 79, Manuel S. Ortiz 80, Zahir Vally 81,82, Ekaterina Pronizius 83,
Martin Voracek 83, Claus Lamm 83, Maurice Grinberg 84, Ranran Li 85,
Jaroslava Varella Valentova 86, Giovanna Mioni 87, Nicola Cellini 87,88,89,90, Sau-Chin Chen 91,
Janis Zickfeld 92, Karis Moon93, Habiba Azab94, Neil Levy 95, Alper Karababa 96,
Jennifer L. Beaudry 97, Leanne Boucher98, W. Matthew Collins 99, Anna Louise Todsen100,
Kevin van Schie 101,102, Jáchym Vintr 103, Jozef Bavolar 104, Lada Kaliska 105, Valerija Križanić 106,
Lara Samojlenko107, Razieh Pourafshari 108, Sandra J. Geiger 109, Julia Beitner 110,
Lara Warmelink 111, Robert M. Ross 112, Ian D. Stephen 112, Thomas J. Hostler 113,
Soufian Azouaghe 114,115, Randy McCarthy116, Anna Szala 117, Caterina Grano 118,
Claudio Singh Solorzano 118, Gulnaz Anjum 119,120, William Jimenez-Leal 121, Maria Bradford121,
Laura Calderón Pérez121, Julio E. Cruz Vásquez121, Oscar J. Galindo-Caballero 121,
Juan Camilo Vargas-Nieto 121, Ondřej Kácha 122, Alexios Arvanitis 123, Qinyu Xiao 124,
Rodrigo Cárcamo125, Saša Zorjan 126, Zuzanna Tajchman127, Iris Vilares 127, Jeffrey M. Pavlacic 128,
Jonas R. Kunst 129, Christian K. Tamnes 129, Claudia C. von Bastian 130, Mohammad Atari 131,
MohammadHasan Sharifian 132, Monika Hricova 104, Pavol Kačmár 104, Jana Schrötter 104,
Rima-Maria Rahal 133, Noga Cohen 134, Saiedeh FatahModarres 135, Miha Zrimsek136,
Ilya Zakharov 137, Monica A. Koehn 138, Celia Esteban-Serna 139, Robert J. Calin-Jageman 140,
Anthony J. Krafnick 140, Eva Štrukelj 141, Peder Mortvedt Isager 142, Jan Urban 143,
Jaime R. Silva 144,145,146, Marcel Martončik 147, Sanja Batić Očovaj 148,149, Dušana Šakan148,149,
Anna O. Kuzminska 150, Jasna Milosevic Djordjevic151, Inês A. T. Almeida 152, Ana Ferreira 152,
Ljiljana B. Lazarevic153, Harry Manley 154, Danilo Zambrano Ricaurte155, Renan P. Monteiro 156,
Zahra Etabari 157, Erica Musser 158, Daniel Dunleavy 159, Weilun Chou160, Hendrik Godbersen 161,
Susana Ruiz-Fernández 161,162,163, Crystal Reeck 164, Carlota Batres 165, Komila Kirgizova 166,
Abdumalik Muminov167, Flavio Azevedo 168, Daniela Serrato Alvarez169, Muhammad Mussaffa Butt 170,
Jeong Min Lee171, Zhang Chen 172, Frederick Verbruggen 172, Ignazio Ziano 173, Murat Tümer 174,
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Christopher Aberson 177, Bence Pálfi178, Mónica Alarcón Maldonado179, Barbora Hubena66,
Asli Sacakli180, Chris D. Ceary15, Karley L. Richard15, Gage Singer181, Jennifer T. Perillo 182,
Tonia Ballantyne183, Wilson Cyrus-Lai 184, Maksim Fedotov 185, Hongfei Du 186,
Magdalena Wielgus187, Ilse L. Pit 188,189, Matej Hruška 190, Daniela Sousa 191, Balazs Aczel 192,
Barnabas Szaszi 192, Sylwia Adamus 33, Krystian Barzykowski 33, Leticia Micheli 193,
Nadya-Daniela Schmidt 194, Andras N. Zsido 195, Mariola Paruzel-Czachura 196, Michal Bialek 197,
Marta Kowal 197, Agnieszka Sorokowska 197, Michal Misiak 197,198, Débora Mola 199,
María Victoria Ortiz199,200, Pablo Sebastián Correa 199, Anabel Belaus 199, Fany Muchembled 201,
Rafael R. Ribeiro 202, Patricia Arriaga 202, Raquel Oliveira 202,203, Leigh Ann Vaughn 204,
Paulina Szwed 205, Małgorzata Kossowska 206, Gabriela Czarnek 207, Julita Kielińska207,
Benedict Antazo 208, Ruben Betlehem 209, Stefan Stieger 210, Gustav Nilsonne 211,212,
Nicolle Simonovic213, Jennifer Taber 213, Amélie Gourdon-Kanhukamwe 214, Artur Domurat 215,
Keiko Ihaya 216, Yuki Yamada 217, Anum Urooj218, Tripat Gill 219, Martin Čadek 220,
Lisa Bylinina 221, Johanna Messerschmidt 222, Murathan Kurfalı223, Adeyemi Adetula 115,224,
Ekaterina Baklanova 225, Nihan Albayrak-Aydemir 226, Heather B. Kappes 227,
Biljana Gjoneska 228, Thea House 229,230, Marc V. Jones 113, Jana B. Berkessel 231,
William J. Chopik 232, Sami Çoksan 233, Martin Seehuus 234, Ahmed Khaoudi 235,
Ahmed Bokkour235, Kanza Ait El Arabi235, Ikhlas Djamai235, Aishwarya Iyer 236, Neha Parashar 236,
Arca Adiguzel 237, Halil Emre Kocalar 237, Carsten Bundt 238,239, James O. Norton240,
Marietta Papadatou-Pastou 241, Anabel De la Rosa-Gomez242, Vladislav Ankushev 36,
Natalia Bogatyreva 36, Dmitry Grigoryev 36, Aleksandr Ivanov 36, Irina Prusova 36,
Marina Romanova 36, Irena Sarieva 36, Maria Terskova 243, Evgeniya Hristova 244,
Veselina Hristova Kadreva244, Allison Janak 245, Vidar Schei 246, Therese E. Sverdrup 246,
Adrian Dahl Askelund 247, Lina Maria Sanabria Pineda 248, Dajana Krupić249, Carmel A. Levitan 250,
Niklas Johannes 29, Nihal Ouherrou 251, Nicolas Say 252, Sladjana Sinkolova253, Kristina Janjić 253,
Marija Stojanovska253, Dragana Stojanovska253, Meetu Khosla 254, Andrew G. Thomas 255,
Franki Y. H. Kung 256, Gijsbert Bijlstra 257, Farnaz Mosannenzadeh 258, Busra Bahar Balci 259,260,
Ulf-Dietrich Reips 261, Ernest Baskin 262, Byurakn Ishkhanyan 263,264,
Johanna Czamanski-Cohen 265,266, Barnaby James Wyld Dixson 267, David Moreau 268,
Clare A. M. Sutherland 269,270, Hu Chuan-Peng 271, Chris Noone272, Heather Flowe 273,
Michele Anne 274, Steve M. J. Janssen 275, Marta Topor 276, Nadyanna M. Majeed 277,
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Mina Nedelcheva-Datsova 283, Nikolay R. Rachev283, Alina Stoyanova283, Kathleen Schmidt 284,
Jordan W. Suchow 285, Maria Koptjevskaja-Tamm 286, Teodor Jernsäther 212,
Jonas K. Olofsson 212, Olga Bialobrzeska 287, Magdalena Marszalek 288, Srinivasan Tatachari 289,
Reza Afhami 290, Wilbert Law 291, Jan Antfolk 292, Barbara Žuro 293, Natalia Van Doren 294,
Jose A. Soto 294, Rachel Searston295, Jacob Miranda 296, Kaja Damnjanović 297, Siu Kit Yeung 298,
Dino Krupić 299, Karlijn Hoyer 300, Bastian Jaeger 300, Dongning Ren 301, Gerit Pfuhl 302,
Kristoffer Klevjer302, Nadia S. Corral-Frías 303, Martha Frias-Armenta 303, Marc Y. Lucas304,
Adriana Olaya Torres176, Mónica Toro 305, Lady Grey Javela Delgado 306, Diego Vega307,
Sara Álvarez Solas 308, Roosevelt Vilar309, Sébastien Massoni 310, Thomas Frizzo310,
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Gabriel Lins de Holanda Coelho 315, Anna Greenburgh 316, Cassie M. Whitt317, Alexa M. Tullett 318,
Xinkai Du 319, Leonhard Volz 319, Minke Jasmijn Bosma320, Cemre Karaarslan 321,
Eylül Sarıoğuz 322, Tara Bulut Allred 323, Max Korbmacher 324, Melissa F. Colloff 325,
Tiago J. S. Lima 326, Matheus Fernando Felix Ribeiro 327, Jeroen P. H. Verharen 328,
Maria Karekla329, Christiana Karashiali330, Naoyuki Sunami 331, Lisa M. Jaremka331,
Daniel Storage 332, Sumaiya Habib333, Anna Studzinska 334, Paul H. P. Hanel 335,
Dawn Liu Holford 335, Miroslav Sirota 335, Kelly Wolfe 335, Faith Chiu 336,
Andriana Theodoropoulou 337, El Rim Ahn338, Yijun Lin 339, Erin C. Westgate 338,
Hilmar Brohmer 340, Gabriela Hofer 340, Olivier Dujols 115, Kevin Vezirian 115, Gilad Feldman 341,
Giovanni A. Trava